designed to create universal user representations for recommender systems,
which consists of two stages: the first involves pre-training on large-scale
datasets to capture universal user behaviors, and the second involves fine-
tuning with supervised labels to adapt the pre-trained representations to
several different tasks. Gu et al. [2021b] presented an approach for learning
universal user representations from unlabeled behavior data. The primary
objective is to generate user representations that encapsulate a wealth of
information, enabling their application across a variety of downstream tasks,
such as predicting user preferences and inferring user profiles. Yuan et al.
[2021] developed a method of user modeling that embodies the “One Person, One
Model, One World” paradigm. This paradigm is designed to create a universal
user representation that can continually learn across different tasks without
succumbing to catastrophic forgetting, which is the loss of previously
acquired knowledge when new information is learned. An innovative technique in
this area has been proposed by Kim et al. [2023], where they introduced a
continual user representation learning method. Their main idea is to use task
embeddings to generate task-specific soft masks, allowing model parameters to
be updated throughout the training sequence and capturing the relationship
between tasks. Additionally, a knowledge retention module with a pseudo-
labeling strategy is introduced to alleviate catastrophic forgetting.

##### Holistic user modeling

Although it can be related to universal user representation as both concepts
aim to create generalized user models, holistic user modeling is about
building a detailed and comprehensive model that takes into account all
relevant aspects of a user’s interaction with a system. This includes not only
the user’s actions but also their expectations, preferences, and limitations.
It is more focused on the integration of various data sources to create a
complete picture of the user. Specific examples can be found in Gong and Wang
[2018] and Gong et al. [2020]. In the former work, they designed a
probabilistic generative model for holistic user behavior modeling in social
media. It integrates opinionated content modeling with social network
structure modeling to capture the consistency and heterogeneity of user
behavior across various user-generated data modalities. In the latter, the
authors proposed a solution that learns user representations in social
networks from both network structure and text content by capturing the
dependency between these two modalities in the latent topic space. This
approach uses statistical topic models to handle the unstructured nature of
the text and embeds both users and topics in a low-dimensional space to
capture their mutual dependency. The user’s affinity to a topic is
characterized by their proximity to the topic’s embedding, which is used to
generate text documents, while the affinity between users is modeled by the
proximity between their embeddings, which is used to generate social network
connections. Another robust line of research in this area has been conducted
by Musto et al., from 2018 and 2021. In Musto et al. [2018], the authors
discussed the development of a holistic user profile framework that integrates
a user’s digital footprints from both social networks and personal devices to
create a comprehensive digital identity. This framework collects data from
platforms such as Twitter and Facebook, as well as from devices like Android
smartphones and FitBit wristbands. The collected data undergoes processing and
enrichment to form a unique user model that reflects the individual’s behavior
and interests. Musto et al. [2020c] presented a knowledge-aware food
recommender system designed to provide personalized recipe suggestions based
on user characteristics and health-related constraints. The system
incorporates a holistic user model to re-rank recipes and was assessed through
a web-based experiment. The findings indicate that holistic user profiles
significantly influence user preferences. Musto et al. [2020a, b, 2021]
discussed the implementation of Myrror and MyrrorBot. Myrror is a platform
designed to create a holistic user model by integrating data from social
networks, smartphones, and wearable devices. The platform’s goal is to
alleviate the problem of information overload and offer personalized services
based on the user’s profile. MyrrorBot is a conversational agent developed to
enable users to interact with and query their user models using natural
language and is built on the Myrror platform. The core components of MyrrorBot
include the Intent Recognizer module, which interprets user requests, and the
Generator module, which formulates responses in natural language. The system’s
conversational interface aims to enhance traditional web interfaces, allowing
for a more intuitive and efficient way for users to access and inspect the
information contained in their profiles.

Figure 3: Specific taxonomy of the Modeling techniques.

####  4.2.6 Modeling techniques

Once the information is organized, the next step is to select suitable
techniques with varying characteristics to build a user profile. This phase is
essentially about the creation of a computational model, which is constructed
using the extracted features and is capable of predicting the needs or
preferences of the user. In the proposed categorization, whose specific
taxonomy is displayed in Figure 3, we present in detail recent relevant works
for each topic. It is also important to note that some techniques may span
multiple categories depending on their specific implementation and use case.
We examine each method within the category in which it is most discussed in
the literature in the context of user modeling.

##### Rule-based

Rule-based user modeling involves creating a set of predefined rules or
conditions that determine user behavior or preferences. These rules are
typically designed based on expert knowledge or domain-specific guidelines
(e.g., [Cheung Chiu and Webb, 1998; Adomavicius and Tuzhilin, 2001; Razmerita
et al., 2003; Brut et al., 2009; Aleven, 2010]). Rule-based approaches are
classified into:

  * •

Expert systems refer to computer programs that emulate the decision-making
ability of a human expert in a specific domain. They use a knowledge base of
rules to draw inferences and make decisions. For example, Rizzo et al. [2016]
explored the development of a rule-based expert system designed to model and
infer mental workload in users, with the aim of enhancing user modeling
techniques. The expert system is constructed by eliciting knowledge from
domain experts and translating this knowledge into computational rules. These
rules are then applied to model user’s mental workloads, using heuristics to
combine them into a numerical index. The system accounts for various
attributes that contribute to the mental workloads, such as mental demand,
temporal demand, effort, performance, and frustration.

  * •

If-Then rules are a form of declarative knowledge representation where
specific conditions (if) are defined to trigger certain actions or conclusions
(then). In the context of user modeling, these rules are used to make
decisions or predictions based on observed user behavior or input features.
Sola et al. [2021] introduced a rule-based recommender system specifically
designed to assist users in the task of business process modeling. In the
context of user modeling, this approach is significant as it adapts to the
user’s input and the structure of the process model at hand. By analyzing a
repository of BPMN 2.0 models, the system learns the common sequences and
dependencies of activities, which reflects the collective expertise and
practices encoded in these models.

  * •

Decision trees involve creating a tree-like model where each node represents a
decision based on input features. They are used for classification and
regression tasks, making decisions by traversing the branches of the tree. To
cite a specific work, Sarker et al. [2020] proposed the Behavioral Decision
Tree (BehavDT) model, which is designed to predict smartphone user behavior by
considering multi-dimensional contexts. Unlike traditional decision tree
models, BehavDT emphasizes behavior-oriented generalization and individualized
preference-oriented decision-making, which enhances its predictive accuracy.
In a recent article, Streeb et al. [2022] provided a comprehensive survey of
visualization techniques for decision trees and rule-based classifiers, with a
focus on user modeling and the tasks associated with the development and
application of these models.

  * •

Ontology-based modeling concerns using ontologies, which define concepts and
relationships in a domain, to represent and reason about user preferences,
characteristics, and behavior. It utilizes ontological rules for making
inferences about users. Rimitha et al. [2018] presented a method for enhancing
personalized job RSs through the development of ontology-based user profiles.
It underscores the significance of user profiles in delivering tailored job
suggestions to individuals by accurately reflecting user preferences and
experiences. Chari et al. [2020] introduced the Explanation Ontology, a
comprehensive model specifically crafted to encapsulate the various facets of
explanations within user-centered AI systems. The model is presented as a
valuable tool for system designers, offering a structured approach to user
modeling with ontology in the context of AI explanations. It emphasizes the
importance of user-centric design in AI and the ongoing efforts to update and
refine the ontology to support the dynamic landscape of user needs and system
capabilities.

##### Neighborhood-based

Neighborhood-based user modeling deals with the idea that users who are
similar to each other in certain aspects are likely to have similar
preferences. It involves analyzing the behavior or characteristics of a user
in relation to their “neighborhood” or a group of similar users (e.g.,
[Konstan et al., 1997; Kim et al., 2011; Meftah et al., 2012; Subramaniyaswamy
and Logesh, 2017; Cami et al., 2019; Sánchez and Bellogín, 2019; Javed et al.,
2021]). We identify the following groups:

  * •

Collaborative filtering relies on user-item interactions and recommendations
from a group of users with similar tastes. It can be user-based, item-based,
or a combination of both. Ma et al. [2022a] investigated the effects of rapid,
population-scale concept drifts, such as those induced by pandemic-like
events, on collaborative filtering models within RSs. The core of the study is
the introduction of the NEST framework, which simulates the evolution of user
needs and behaviors in response to non-stationary external events. Wang et al.
[2022a] introduced a recommender system framework known as Time-aware
Attention-based Deep Collaborative Filtering (TADCF), whose core is its
ability to model dynamic user preferences over time.

  * •

Content-based filtering111Content-based filtering is placed under the
“Neighborhood-based” category to highlight its similarity to collaborative
filtering in terms of creating a neighborhood of similar items. In content-
based filtering, this neighborhood is formed based on the content features of
items and the user’s historical interactions. The placement acknowledges that,
in a sense, content-based filtering creates a “content-based neighborhood” for
recommendations. However, it is important to note that content-based filtering
is often considered a separate and distinct approach due to its emphasis on
item characteristics and individual user profiles. recommends items based on
the user’s past preferences and the characteristics of the items. It considers
the content features of items and user profiles. To offer a concrete example,
Pujahari and Sisodia [2022] presented a wide approach to enhancing content-
based user modeling in recommender systems. For user profile generation, the
paper introduces an ensemble classifier that operates within an iterative
learning framework. This classifier is designed to incrementally learn and
update user preferences, allowing the system to adapt to user interactions and
feedback over time. The iterative update rules ensure that the user profiles
evolve, capturing the dynamic nature of user interests.

  * •

K-Nearest Neighbors (KNN) is a technique that classifies or recommends based
on the majority class or average of the k-nearest data points in feature
space. For instance, Nagaraj et al. [2022] developed a recommender system
aimed at aiding undergraduate and graduate students in selecting the most
suitable colleges based on their individual profiles. The system employs user
modeling through the KNN algorithm to calculate weighted scores for college
recommendations. By analyzing factors such as SAT scores, tuition fees, and
academic records, the KNN algorithm helps to match students with institutions
where they have the highest likelihood of acceptance and success.

##### Statistical modeling

Statistical modeling for user behavior refers to the use of statistical
methods to analyze and model patterns in user data, along with understanding
relationships and making predictions (e.g., [Horvitz et al., 1998; Zukerman
and Albrecht, 2001; Zigoris and Zhang, 2006; Zhang and Koren, 2007; Harvey et
al., 2011; Seroussi et al., 2011; Benmakrelouf et al., 2015; Tomeo et al.,
2015; Vu et al., 2015; Yadav and Selvakumar, 2015; Chen et al., 2019a]). This
category includes:

  * •

Regression analysis involves modeling the relationship between a dependent
variable and one or more independent variables. It is used to understand and
predict the impact of changes in the independent variables on the dependent
variable. To cite a relevant paper, Gao et al. [2017] introduced an item
recommendation model named CDUE, which stands for Collaborative Dynamic User
profile Evolution. This model is designed to predict a user’s future interests
by incorporating a regression approach that accounts for the temporal dynamics
of both user interests and item topics. Specifically, the model employs a
dynamic sparse topic regression to track how item topics evolve over time,
ensuring that the recommendations remain relevant as the content changes.

  * •

Probabilistic modeling concerns the use of probability distributions to
represent uncertainty and variability in user behavior or preferences. This
approach allows for the incorporation of statistical methods, likelihoods, and
estimation techniques to capture the inherent uncertainty in modeling user
characteristics based on observed data. Probabilistic modeling encompasses a
variety of statistical techniques that utilize probability theory to model and
understand complex relationships within user data. To illustrate with
particular examples, Hadoux and Hunter [2018] introduced a framework for
probabilistic user modeling in the context of persuasive argumentation. The
core of the framework is the use of beta distributions to represent the
uncertainty in user beliefs, which is a departure from traditional models that
use sharp, deterministic values. An intriguing approach has been proposed in
Vardasbi et al. [2020] and Oosterhuis and de Rijke [2021]. A key aspect of the
papers is the use of probabilistic user models for simulating user
interactions in the experimental setup. These models are crucial for
generating data that reflects the biases present in real-world scenarios. By
employing probabilistic user models, the authors are able to create a more
realistic testing environment for their proposed affine estimator.

  * •

Differentiable user models are computational constructs that enable efficient
probabilistic user modeling by providing a differentiable approximation of
cognitive behavior simulators, which are often non-differentiable and
computationally prohibitive for practical applications. These models are
designed to be compatible with modern machine learning frameworks, often
implemented using neural networks, and allow for real-time applications even
with advanced cognitive models that lack a closed-form likelihood. The most
important work on this topic has been written by Hämäläinen et al. [2023],
where the authors introduced this innovative approach to cognitive user
modeling by employing differentiable surrogates, which significantly enhance
computational efficiency and enable real-time applications. The differentiable
surrogates are trained offline and are designed to be utilized online,
effectively addressing the computational challenges that have traditionally
limited the use of probabilistic user modeling based on cognitive models. The
differentiable user models are shown to overcome the limitations of existing
likelihood-free inference methods, providing a solution that is both
computationally viable for online applications and maintains user modeling
accuracy.

  * •

Bayesian models specifically adhere to the principles of Bayesian probability
theory to update beliefs about user preferences or behavior based on new
evidence. They provide a framework for incorporating prior knowledge and
updating it with observed data. Shen et al. [2021a] developed a user profiling
system for inferring gender and personality traits from non-linguistic audio
data during conversations. The core component of the system is a voice
activity detection (VAD) mechanism and a feature extraction process that
focuses on conversational dynamics. The VAD component utilizes a Bayesian
algorithm to distinguish between speech and non-speech segments within the
audio data, which is crucial for accurately capturing the conversational
features relevant to user profiling.

  * •

Factor analysis is a statistical method used to identify latent (i.e.,
unobservable) factors that explain patterns of correlations within observed
variables. It is often applied to uncover underlying dimensions in user
behavior or preferences. An interesting work has been presented by Yuan et al.
[2020b]. The paper introduces a Generalized and Fast-converging Non-negative
Latent Factor (GFNLF) model designed to predict user preferences in
recommender systems. The GFNLF model aims to overcome the limitations of slow
convergence and restricted representational capabilities observed in
traditional latent factor models.

##### Data mining

Data mining refers to the process of discovering patterns, associations, and
trends within large datasets. Techniques like association rule mining and
clustering are used to extract valuable information and insights from user-
related data (e.g., [Eirinaki and Vazirgiannis, 2003; Pierrakos et al., 2003;
Mobasher, 2007; Xie and Yu, 2009; Angeline, 2013; Erlandsson et al., 2016;
Burgos et al., 2018; Sarker and Salim, 2018; Abu Sulayman and Ouda, 2019;
Shazad et al., 2019]). The topics pertaining to data mining are:

  * •

Association rule learning is a technique designed to unveil meaningful
relationships, patterns, or associations among variables (in our context, user
behaviors or preferences) within extensive datasets. This method seeks to
identify rules expressing correlations or co-occurrences between different
user actions or characteristics. To provide some samples, Si et al. [2019]
presented an innovative use of association rules to uncover and analyze the
interests of users on social networks, with a particular focus on Twitter and
LinkedIn. For Twitter, the authors propose a method that reorders a user’s
interests based on association rules derived from user behavior. On LinkedIn,
the researchers collected profile data to identify the distribution of user
interests. They applied association rules to this dataset and discovered a
substantial number of correlations between various human interests. Agouti
[2022] proposed an innovative algorithm named Diffusion-Graph-Based Influence
Maximization (DGIM), which leverages association rule mining to model the
spread of influence and to detect relationships between users concerning
specific topics.

  * •

Apriori algorithm is specifically tailored association rule learning in user
modeling and is employed to identify frequent item sets and generate
association rules. Following the “apriori” principle, it iteratively refines
rules based on user behavior, helping to reveal patterns and associations in
interactions or preferences. Singh et al. [2021] developed an optimized
collaborative filtering algorithm for recommenders that utilizes the apriori
algorithm to enhance user profile creation. The paper details how the apriori
algorithm is employed to generate detailed user profiles by analyzing users’
interactions with items. These profiles are then used to modify the similarity
measure for finding similar neighbors, which is a critical step in
collaborative filtering algorithms.

  * •

Text mining involves extracting meaningful information and patterns from
unstructured text data. It includes techniques such as natural language
processing, sentiment analysis, and text clustering to understand and model
user preferences expressed in textual form. Greco and Polli [2020] explored
the use of Emotional Text Mining (ETM) as a tool for user profiling in the
context of brand management, with a specific focus on analyzing Twitter
messages about a sportswear brand. The paper demonstrates how ETM can be
leveraged for user profiling in brand management, providing theoretical and
managerial insights. It showcases the method’s ability to simplify the
analysis of large textual datasets and its contribution to the field through
the extraction of actionable information for enhancing customer understanding
and brand strategy. Heidari et al. [2020] presented, in the same domain of
social networks, an approach to detecting social bots on Twitter by focusing
on user profiling extracted from tweet text.

  * •

Sequence mining focuses on discovering patterns or relationships within
sequential data. It is commonly used to identify sequences of events or
actions in a user’s interactions over time, providing insights into behavior
patterns. An interesting approach has been proposed by Gurbanov and Ricci
[2017]. They developed a hybrid recommender system that integrates sequence
mining with collaborative filtering to enhance the prediction of user actions
on an online career-oriented social networking platform. The focus of the
system is to utilize the sequential patterns of user interactions,
particularly with job postings, to forecast specific actions such as replying
to a job posting. The sequence mining component is crucial in the hybrid model
as it is responsible for predicting the probability of the next user action
based on the observed sequence of interactions.

  * •

Anomaly detection, in user modeling, aims to identify instances that deviate
significantly from the expected or normal behavior. It involves detecting
unusual patterns, outliers, or anomalies in user data that may indicate
fraudulent activity, errors, or novel user behavior. To supply detailed
instances, Böhmer and Rinderle-Ma [2020] presented an extended study on
anomaly detection for temporal data in business processes, with a focus on
user modeling. The authors propose a novel approach that leverages association
rule mining to detect anomalies in process runtime behavior, aiming to
differentiate between benign and malign anomalies, minimize false positives,
and elucidate the root causes of anomalies to users. Sharma et al. [2020]
introduced a procedure for anomaly detection in user behavior analytics by
employing an unsupervised method. The primary objective is to identify insider
threats by analyzing patterns in user session activities.

##### Machine learning

Machine learning (ML) refers to a set of computational techniques that empower
systems to automatically learn patterns, relationships, and predictions from
user data and behaviors. Generally, ML methods are divided into two groups:

  * •

Supervised learning involves training algorithms on labeled datasets to
predict or classify user behavior based on provided examples, enabling the
system to make accurate predictions for new user interactions (e.g., [Cufoglu
et al., 2008; Tang et al., 2010; Santra and Jayasudha, 2012; Raghuram et al.,
2016]). Specific applications in user modeling make use of the following
supervised learning algorithms:

    * –

Support Vector Machines (SVM) are models that can be used for classification
and regression tasks. They find a hyperplane that best separates classes or
predicts a target variable. Ahmad et al. [2021] introduced a method for
identifying spam tweets on Twitter by leveraging an SVM classifier, with a
particular focus on analyzing user interactions. In this study, it is applied
to differentiate between spam and non-spam tweets using a variety of features,
including those derived from user interactions.

    * –

Random Forest is an ensemble learning method that constructs a multitude of
decision trees during training and outputs the class that is the mode of the
classes from individual trees. Chen et al. [2021a] presented a study that
evaluates the effectiveness of the random forest algorithm in identifying
high-risk users within public opinion discussions on digital platforms. In
particular, is used to classify users based on their risk of contributing to
negative public opinion.

    * –

Boosting algorithms are a set of ensemble learning techniques that combine the
predictions of multiple weak models to create a robust and accurate predictive
model. These algorithms sequentially train weak learners, with each subsequent
model focusing on the mistakes of its predecessors. Popular boosting
algorithms include AdaBoost and Gradient Boosting, which are applied to
capture complex relationships in user data. An extensive study on the
application of boosting algorithms for user profiling, specifically for
predicting student performance in higher education, has been presented by
Hamim et al. [2022]. To achieve this, the analysis compares different research
studies that utilize boosting algorithms for student profile modeling. The
paper also explores the impact of student behaviors and characteristics on
their academic performance. It proposes a student profile model that leverages
educational data mining techniques to predict, classify, and adaptively
support students’ learning processes. This model can also be used for e-RSs
that tailor educational content to individual student needs.

    * –

Naïve Bayes is a probabilistic algorithm based on Bayes’ theorem. It assumes
that features are conditionally independent, making it efficient and effective
for classification tasks. A valuable research paper in the student profiling
domain has also been provided for this specific algorithm. Specifically,
Tripathi et al. [2019] presented a study on student profile modeling, with a
particular focus on predicting student performance. The core of the study is
the application of the Naïve Bayes classifier, which is compared against the
existing Support Vector Machine (SVM) classifier. The results indicate that
the Naïve Bayes classifier outperforms the SVM in terms of both accuracy and
execution time, making it a more efficient and effective tool for predicting
student performance.

  * •

Unsupervised learning harnesses input data devoid of explicit labels to
uncover inherent patterns or structures within user behavior, providing
valuable insights into the underlying data structures without the need for
predefined categories or outcomes (e.g., [Castellano et al., 2007; Boratto and
Carta, 2014; Benmakrelouf et al., 2015; van Dam and van de Velden, 2015;
Cañigueral and Meléndez, 2021; Yang et al., 2021]. Algorithms belonging to
this group are:

    * –

Cluster analysis (or simply clustering) groups data points based on
similarities, creating clusters or groups of similar instances. Common
algorithms include k-means clustering and hierarchical clustering. Clustering
helps identify user segments with similar behavior or preferences. To provide
concrete examples, Ouaftouh et al. [2019] proposed a clustering-based approach
to enhance social recommender systems. The core of the research is the
application of partitional clustering algorithms, with a focus on the K-means
algorithm, to group user profiles into clusters. These clusters are intended
to represent users with similar interests and preferences, which can then be
used to provide more accurate recommendations within an e-commerce
environment.

    * –

Dimensionality reduction techniques reduce the number of features in the
dataset while retaining its essential information. Reducing dimensionality
aids in visualizing user data patterns and improving computational efficiency.
Principal Component Analysis (PCA) is the most popular method of this type. Bi
et al. [2016] introduced an anomaly detection model to scrutinize user
behavior within database systems and web browsing environments. PCA analyzes
user behavior by calculating covariance matrices and extracting significant
behavior features. This statistical approach simplifies the complexity of user
behavior data, allowing the model to distinguish between normal and abnormal
patterns effectively.

##### Deep learning

Deep learning (DL) involves using deep neural networks (i.e., neural networks
with multiple layers) to capture complex patterns and representations in user
data. It excels at handling large and unstructured user data, making them
suitable for tasks such as user behavior prediction and personalized
recommendations. As observed in various fields, plenty of articles have been
published in user modeling employing several DL techniques and architectures
(e.g., [Elkahky et al., 2015; An et al., 2019; Ren et al., 2019; Heidari et
al., 2020; Zhou et al., 2020b; Wang et al., 2021b; Wen et al., 2021; Zhao et
al., 2021; Zhou et al., 2023]). A description of these methods and examples of
contributions for each of them are provided below. It is important to
highlight that a single approach can include more than one category. We
consider these cases only under one topic.

  * •

Attention networks (or mechanisms) allocate varying degrees of importance to
different parts of the input sequence. This enables the model to focus on
specific aspects of user behavior, enhancing its ability to capture and
understand intricate patterns in the data (e.g., [Fazil et al., 2021; Chu et
al., 2022; Qi et al., 2022b]). To detail specific works, Qi et al. [2021]
introduced TRISAN (Trilateral Spatiotemporal Attention Network), an attention-
based neural model designed for user behavior modeling in the context of
location-based search. TRISAN is unique in its ability to incorporate temporal
relatedness into the analysis of user behavior, particularly in how it relates
to the geographic proximity of items and user requests. This is achieved
through a specialized fusion mechanism that considers not only physical
distance but also semantic similarity to model geographic closeness. Wang et
al. [2020c] and Wang et al. [2022b] combines graph neural networks and
attention mechanisms. In the former article, the authors presented UIL-HGAN, a
model for user identity linkage across social networks, emphasizing the
enhancement of user modeling. UIL-HGAN stands for User Identity Linkage using
a Heterogeneous Graph Attention Network, a method that integrates user
profiles, user-generated content, and structural information within a
heterogeneous social network graph. In the latter, an approach to enhance user
profiling in the context of personalized point-of-interest (POI)
recommendations is proposed. The paper introduces the Spatial-Temporal Graph
Convolutional Attention Network (STGCAN), a model that leverages a knowledge
graph with temporal information to capture the dynamic and evolving
preferences of users with respect to various locations.

  * •

Graph Neural Networks (GNNs) are designed to handle graph-structured data,
representing relationships between entities. They are proficient in capturing
the complex interdependencies within user networks or interaction graphs,
providing a comprehensive view of user relationships and preferences (e.g.,
[Chen et al., 2019b; Hu et al., 2020; Wang et al., 2020a, c; Chen et al.,
2021b; Dai and Wang, 2021; Wu et al., 2020a; Xia et al., 2021a, b; Yan et al.,
2021; Luo et al., 2022; Wang et al., 2022b; Zhao et al., 2022]). To provide
specific instances, Agarwal et al. [2022] introduced SEINE (Spam Detection
using Interaction Networks), a graph neural network model tailored for
identifying spam users on web-scale social media platforms. SEINE’s primary
innovation lies in its sophisticated user behavior and interaction modeling,
which is achieved through the construction of a user-entity graph. This graph
is not merely a collection of nodes and edges but a rich, heterogeneous
network that captures the multifaceted interactions between users and various
entities such as posts, comments, and likes. Han et al. [2022] developed the
Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network (MTHGNN), a
model for personalized micro-video recommendation, with a strong emphasis on
user modeling. The MTHGNN is designed to capture the dynamic and multi-faceted
nature of user preferences in the context of micro-video content, such as that
found on platforms like TikTok. Yan et al. [2022] presented a framework named
Interaction-aware Hypergraph Neural Networks (IHNN) specifically designed for
enhancing user profiling on e-commerce and social media platforms. IHNN
employs hypergraph and meta-path based graphs to effectively model user
interactions and attributes. The framework is constructed with a multi-view
hypergraph approach and utilizes convolutional operations to aggregate high-
order user information comprehensively. It also incorporates a semi-supervised
learning approach for predicting user profiles. This method is particularly
useful for tasks such as predicting user characteristics like age and gender.
Cheng et al. [2023] introduced a model named Multi-Behavior Recommendation
with Cascading Graph Convolution Networks (MB-CGCN), designed to enhance
recommendations by leveraging multiple user behaviors. The architecture is
structured around a sequence of Graph Convolutional Network (GCN) blocks, each
representing a different user behavior in a behavior chain. The key innovation
of MB-CGCN lies in its ability to capture the dependencies between various
behaviors to improve the accuracy of recommendations.

  * •

Convolutional Neural Networks (CNNs) leverage convolutional layers to learn
hierarchical features and spatial relationships within sequential or tabular
data. While traditionally used in image processing, CNNs can be adapted to
understand complex patterns in user interactions, identifying important
temporal and contextual features for effective representation learning in user
behavior (e.g., [Karatzoglou et al., 2018; Wang et al., 2019b; Wanda and Jie,
2020; Cura et al., 2021; Mekruksavanich and Jitpattanakul, 2021]). Other
contributions are, for example, Yuan et al. [2021], who introduced Conure, a
continual learning framework that utilizes a Temporal Convolutional Network
(TCN) architecture to represent users. It is specifically engineered to handle
sequential tasks and is adept at transferring knowledge from one task to
another while retaining important information from past tasks. Qi et al.
[2022b] presented an approach to personalized news recommendation through a
Candidate-aware User Modeling (CAUM) framework. By using a candidate-aware
self-attention network, a candidate-aware convolutional neural network, and a
candidate-aware attention network, the CAUM framework is designed to capture
both global and short-term user interests that are specifically relevant to
the candidate news items. This convolutional neural network is adapted to
process user behavior data with a focus on candidate news, helping to capture
local and sequential patterns that are indicative of user interests.

  * •

Autoencoders are neural network architectures designed for unsupervised
learning. They aim to reconstruct input data, and in the context of user
modeling, autoencoders can be employed for tasks such as feature learning and
data compression to uncover latent representations in user behavior (e.g.,
[Rajashekar et al., 2016; Abu Sulayman and Ouda, 2019; Wang et al., 2019b; Pan
et al., 2020; Sharma et al., 2020]). Noteworthy specific publications include
the work by Deng et al. [2022b], who introduced a user behavior analysis model
that leverages stacked autoencoders for dimensionality reduction and feature
selection in the context of a complex power grid environment. The model is
designed to optimize resource coordination and planning by analyzing user
behavior characteristics. Fazelnia et al. [2022] developed a user modeling
approach named FS-VAE, which employs a variational autoencoder (VAE) framework
that leverages user-item interaction data to learn effective user
representations in the music recommender systems domain. Liu et al. [2023c]
proposed Hierarchical Personalized Federated Learning (HPFL), a model designed
to tackle a number of heterogeneity challenges. In its “augmented” version
(i.e., AHPFL), the authors incorporate augmented mechanisms to filter out low-
quality information and integrate high-quality information, such as singular
value decomposition (SVD) and autoencoders, to improve the effectiveness of
user models.

  * •

Recurrent Neural Networks (RNNs) are tailored for sequential data, capturing
dependencies over time. Their cyclic connections make them adept at
understanding the temporal aspects of user behavior, making them suitable for
tasks involving sequential interactions (e.g., [Carvalho et al., 2017;
Ishitaki et al., 2017; Ni et al., 2018; Tripathi et al., 2018; Yu et al.,
2019; Gu et al., 2020]). Interesting works can be found, for instance, in
Donkers et al. [2017], where the authors proposed an approach to sequential
recommender systems by incorporating user-specific information into RNNs. The
paper introduces a user-based RNN model that personalizes recommendations by
considering the unique preferences and behavioral patterns of individual
users. To achieve this, they extend traditional RNNs with a gated architecture
using Gated Recurrent Units (GRUs), which facilitates the integration of user
data into the sequential learning process. Ge et al. [2018] designed a
framework to personalize search engine results by leveraging a hierarchical
recurrent neural network (HRNN) with a query-aware attention mechanism. The
framework’s objective is to utilize sequential data from users’ past
interactions to construct dynamic user profiles, thereby enhancing the
personalization of search results. Chu et al. [2022] presented a methodology
for predicting student performance in online learning environments, with a
focus on enhancing user modeling across diverse demographic groups such as
race and gender. Utilizing RNNs, specifically an attention-based Gated
Recurrent Unit (GRU) with a self-attention mechanism, the model predicts
course passing likelihood based on students’ activity sequences. Li et al.
[2022b] implemented a user profiling procedure that combines semantic behavior
modeling with RNNs by proposing a methodology that enriches user behavior data
with semantic information to enhance the accuracy of user profiling.

  * •

Long Short Term Memory (LSTM) represents a specialized form of recurrent
neural network. LSTMs address the vanishing gradient problem, enabling
effective modeling of long-term dependencies in sequential user data, leading
to an improved understanding of user preferences over time (e.g., [Zhu et al.,
2017; Żołna and Romański, 2017; Singh et al., 2019; Sharma et al., 2020; Cura
et al., 2021; Fazil et al., 2021]). To cite specific relevant contributions,
Gu et al. [2020] developed a hierarchical user profiling framework, whose core
is the integration of Pyramid RNNs with a specialized component known as
Behavior-LSTM. The Behavior-LSTM layer is particularly crucial as it models
the temporal sequence of user behaviors, providing a dynamic and evolving
representation of user interests. Another valuable LSTM-based framework, named
Time Information Enhanced Personalized Search (PSTIE), has been proposed by Ma
et al. [2020]. The PSTIE framework utilizes time-aware LSTM architectures to
model the evolution of user interests over time. This allows the model to
calculate both short-term and long-term query intent and document interest.
The framework also includes techniques for re-ranking by combining these
interest representations. Sahoo and Gupta [2021] presented a system for
detecting fake news on Facebook by leveraging user profile features alongside
news content features. The approach involves using LSTM to effectively process
and learn from the user profile features, which are indicative of the
credibility of the news shared. Nkambou et al. [2023]

  * •

Transformers utilize attention mechanisms to capture contextual information
bidirectionally. They succeed in natural language understanding, providing
rich representations of user-generated text data for various applications in
user modeling (e.g., [Huertas-García et al., 2021; Zhu et al., 2021]). A
specific case is provided by Avny Brosh et al. [2022], who introduced a neural
network-based model, BRUCE, which focuses on personalized bundle
recommendations. The main ability of BRUCE is modeling user preferences and
the latent relationships between items within a bundle using a Transformer-
based architecture. This architecture employs self-attention mechanisms to
create contextualized item embeddings that reflect both the individual items’
characteristics and their interactions within the bundle. The most important
transformer-based model is BERT, which stands for Bidirectional Encoder
Representations from Transformers (examples of BERT-based models in user
modeling can be seen in Gu et al. [2021a] and Kota et al. [2021]). To provide
some detailed instances, Wu et al. [2022] presented UserBERT, an approach for
pre-training user models on unlabeled user behavior data. This method employs
contrastive self-supervision to capture universal user information, which is
crucial for enhancing personalization in various applications. UserBERT
introduces two key self-supervision tasks: Masked Behavior Prediction,
involving predicting user behaviors that have been intentionally masked in the
input data, and Behavior Sequence Matching, required to determine whether two
sequences of user behavior are from the same user, promoting the model’s
ability to discern patterns and similarities in user activities. To further
refine the pre-training process, UserBERT incorporates a medium-hard negative
sampling method. This technique selects negative samples that are neither too
easy nor too difficult for the model to discriminate, providing an optimal
challenge for learning. Zheng et al. [2022a] developed the Personalized Emoji
Recommendation with Dynamic user preference (PERD) model. The core of PERD is
a text encoder that utilizes a BERT model to capture the semantic
representations of tweets. This allows the system to understand the context
and content of user tweets effectively. Alongside the text encoder, the model
incorporates a personalized attention mechanism. This mechanism is crucial as
it sifts through a user’s historical tweets to identify and emphasize those
that are most informative for determining the user’s preferences.

####  4.2.7 Performance evaluation

The evaluation phase is a critical step in any modeling problem as it allows
for the assessment of a model’s performance and its suitability for the given
task. This phase is particularly important in user profiling, where standard
evaluation measures are used to assess the performance of developed
approaches. Common techniques for model evaluation include performance metrics
like accuracy, precision, recall, F-measure (also known as F1 score), mean
reciprocal rank (MRR), cosine similarity, and Jaccard similarity [Kaur et al.,
2018; Costanzo et al., 2019; Eke et al., 2019].

Presently, there exist two principal approaches for assessing the efficacy of
independent user profiling methods: (1) relating them to classification tasks
and (2) adopting ad-hoc generated simulated data.

##### Evaluation as a classification task

This strategy adheres to conventional evaluation protocols and entails
appraising the proposed model or method through its proficiency in a
classification task. Typically, this method employs machine learning
techniques to classify user profiles, discerning between authentic and non-
authentic ones. By subjecting the model to this classification task, it
becomes possible to gauge its effectiveness in accurately identifying and
categorizing users based on their personal attributes. This process is
essential in understanding the model’s performance and reliability in the
context of user profiling. Clear recent demonstrations of user modeling
methods assessed as a classification task can be found, for instance, in Chen
et al. [2021b], Dai and Wang [2021], Yan et al. [2021], and Purificato et al.
[2022].

##### Evaluation with simulated data

The generation of simulated data is a means to curtail the volume of actual
user data collected. This approach is designed not only to streamline the
efficiency of profiling but also to uphold the privacy and confidentiality of
users’ personal information. By creating synthetic datasets that emulate the
characteristics and patterns found in genuine user data, practitioners can
significantly diminish the reliance on actual user information. This reduction
in the dependence on real data serves a dual purpose: it mitigates potential
privacy concerns by limiting the exposure of sensitive information, while
concurrently ensuring that the profiling process remains effective and
accurate. A comprehensive methodology for simulating and evaluating user
interactions within search sessions has been presented by Zerhoudi et al.
[2022]. The authors introduce the use of first-order Markov models and
contextual Markov models as tools to replicate user search behavior. The
realism of these simulated interactions is assessed using two main methods:
Kolmogorov-Smirnov test and classification-based evaluation. The findings
indicate that the simulated sessions closely resemble the real user log
sessions, suggesting that the simulated interactions are representative of
actual user behavior. In Keurulainen et al. [2023], the authors proposed an
“amortised experimental design” for user models. The paper explores the use of
Bayesian optimal experimental design and reinforcement learning to create a
user model described as a simulation-based reinforcement learning model.

Take-home messages from building and modeling user profiles.  • Variety of
user modeling methodologies: A range of methods for creating user profiles is
explored, including rule-based modeling that uses predefined rules, ontology-
based modeling for structured knowledge representation, and collaborative
filtering for leveraging user community data to predict individual
preferences. • Advanced analytical techniques in user modeling: Highlights the
use of statistical modeling, machine learning, and deep learning techniques.
These methods are crucial for analyzing complex user data, identifying
patterns, and making accurate predictions about user behavior, along with
preferences, interests, and intentions. • Importance of the evaluation phase:
Emphasizes the essential role of the evaluation phase in user modeling. This
phase assesses the performance and appropriateness of models, using standard
measures to ensure their reliability and effectiveness in representing and
predicting user behavior.

###  4.3 Beyond-accuracy approaches

The integration of advanced techniques surpassing mere accuracy represents a
substantial paradigm shift across diverse domains, notably influencing user
modeling and profiling. These methodologies go beyond the traditional emphasis
on predictive precision, placing greater importance on core principles that
also consider the ethical dimensions of user modeling, focusing on issues like
privacy, transparency, and the responsible use of data. This broader view
encourages the development of models that not only predict user actions
accurately but also contribute positively to the user’s overall interaction
with a system, fostering a more holistic and user-centric approach to
modeling. Within this context, we identify relevant contributions belonging to
three topics: explainable user modeling, fair user modeling, and privacy-
preserving user modeling (specifically referring to methods leveraging
federated learning method).

####  4.3.1 Explainable user modeling

Balog et al. [2019] introduced a set-based recommendation technique that
emphasizes the importance of user models that are transparent, scrutable, and
explainable. Transparency is achieved by providing users with insights into
how their preferences are understood by the system and how the recommendation
process works. Scrutability is incorporated by allowing users to directly and
meaningfully revise their model. Explainability is provided to the level of
user preferences, rather than just explaining why a given item was
recommended, as commonly done in related works. De Pauw et al. [2022] proposed
a recommender system, named TEASER, which is particularly geared towards
enhancing the user experience by providing recommendations that are not only
relevant but also accompanied by detailed explanations. The transparency and
interpretability of TEASER stem from its ability to articulate the reasoning
behind its suggestions. Guesmi et al. [2022] focused on how transparency, user
control, and personal characteristics influence the adoption of recommendation
agents. The research introduces the Recommendation and Interest Modeling
Application (RIMA), a tool designed to enhance transparency by providing
explanations of user interest models at varying levels of detail. The study
assesses the effectiveness of different explanation levels within the system
and how they are perceived by users with diverse personal characteristics.
Other examples of works addressing explainability in user modeling can be seen
in Huang et al. [2019], Wang et al. [2019a], Chari et al. [2020], Hase and
Bansal [2020], Xian et al. [2021], Minn et al. [2022], and Ding et al. [2023].

####  4.3.2 Fair user modeling

Dai and Wang [2021] designed FairGNN, a framework to ensure fairness in GNN-
based user profiling (evaluated as a node classification task). The framework
employs adversarial debiasing, where the adversary’s goal is to ensure that
the sensitive attributes cannot be accurately predicted from the node
representations, thereby encouraging the GNN to learn fair representations.
The theoretical analysis provided in the paper supports the effectiveness of
this approach, showing that FairGNN can guarantee fair representations under
specific conditions. To further promote fairness, a regularizer is added to
the GNN classifier, and a covariance constraint is introduced to control the
correlation between the sensitive attributes and the learned representations.
These measures help to ensure that the predictions made by the GNN are not
unfairly influenced by sensitive attributes. Within the same context of GNN-
based user profiling models, Purificato et al. [2022] conducted a thorough
investigation into the fairness of these models. The study specifically
focuses on behavioral user profiling, which is a critical aspect of
personalized services in various online platforms. The core of the research
revolves around evaluating the fairness of these models in terms of how they
might discriminate against certain user groups. To quantify fairness, the
paper employs several disparate impact and disparate mistreatment metrics,
utilizing two real-world datasets from e-commerce platforms in their
experiments. This work is followed up by the implementation of a unified
framework, i.e., FairUP [Abdelrazek et al., 2023], that allows researchers and
practitioners to interact with state-of-the-art GNN-based models for user
profiling and evaluate fairness on several graph data sources. Purificato and
De Luca [2023] addressed the topic of algorithmic fairness within the realm of
user profiling. It identifies a gap in current fairness analysis, which is
predominantly applied to binary classification problems and relies on the
absolute difference in fairness metrics. This approach, the authors argue, can
result in evaluations that do not accurately reflect the fairness of a system.
To illustrate their point, the authors present a case study on the use of GNN-
based models for user profiling. They discuss the limitations of current
practices and the potential ethical consequences that arise from these
limitations. The paper aims to spark conversation within the academic
community about these issues and to encourage more comprehensive and nuanced
analyses of fairness in algorithmic systems. Another interesting framework for
enhancing fairness in user modeling is proposed by Zhang et al. [2023b]. The
framework employs an adversarial learning approach that includes a filter
module and a discriminator module to minimize the mutual information between
user representations and sensitive attributes. FairLISA operates by combining
data with both known and unknown sensitive attributes. It consists of two main
components: a filter that aims to remove sensitive information from user
representations and a discriminator that attempts to detect any remaining
sensitive information. Further contributions in this domain are Shen et al.
[2021b], Chu et al. [2022], and Zheng et al. [2022b].

####  4.3.3 Privacy-preserving user modeling

The most relevant recent works about privacy-preserving in user modeling
employ federated learning approaches. Chu et al. [2022] introduced a
methodology for predicting student performance in online learning
environments. The core of the procedure is a personalized federated learning
framework that allows for the creation of individualized models for student
subgroups, derived from a global model that aggregates data across all
students. Liu et al. [2023c] proposed a federated learning technique for user
modeling in environments characterized by inconsistent clients. The proposed
approach, Hierarchical Personalized Federated Learning (HPFL), is designed to
tackle the challenges of statistical heterogeneity, privacy heterogeneity,
model heterogeneity, and quality heterogeneity that are prevalent in such
settings. Zhang et al. [2023a] presented the Personalized Federated
Recommendation (PFedRec) framework, a system that leverages federated learning
to create user profile-specific recommendation models. PFedRec is designed to
be deployed on smart devices, enabling a decentralized and privacy-preserving
method for capturing user preferences. The proposed federated learning
approach is centered around a dual personalization mechanism that allows for
fine-grained personalization at both the user and item levels. This is a
significant departure from traditional federated learning methods, which often
focus on a single model that is shared across all users. Instead, PFedRec
formulates a unified federated optimization framework that learns individual
lightweight models tailored to each user’s unique preferences. Privacy-
preserving user modeling techniques have also been presented by Wu et al.
[2021b] and Luo et al. [2022].

Take-home messages from beyond-accuracy approaches.  • Ethical advancements in
user modeling: A shift towards advanced user modeling techniques emphasizes
ethical principles, focusing on privacy, transparency, and responsible data
use. This approach aims to enhance both the accuracy and the overall user-
system interaction. • Three key beyond-accuracy approaches: – Explainable user
modeling emphasizes the importance transparency and user understanding in
modeling processes; – Fair user modeling focuses on creating unbiased models
that prevent discrimination against specific user groups; – Privacy-preserving
user modeling focuses on safeguarding privacy through techniques such as
federated learning.

###  4.4 Applications

User modeling and profiling techniques play a crucial role in various domains
and applications, with different data being extracted and utilized based on
the purpose and collection methods. User models, historically integral only to
recommender systems and any kind of personalized platforms, are now widely
employed across various applications.

####  4.4.1 Recommender systems

A recommender system, belonging to the category of information filtering
systems, aims to predict item ratings or user preferences. The key components
of a personalized recommender system include the efficient understanding of
the user and recommending items aligned with their interests. Recommender
systems always benefited from user profiling methods [Zimmerman and Kurapati,
2002; Middleton et al., 2004; Liu et al., 2007; Zhang and Koren, 2007;
Berkovsky et al., 2008; Adomavicius and Tuzhilin, 2011; Lakiotaki et al.,
2011; Konstan and Riedl, 2012; Liu, 2015]. Significant modern systems in this
field naturally leverage the power of DL architectures (e.g., [An et al.,
2019; Yu et al., 2019; Jin et al., 2020; Pan et al., 2020; Xia et al., 2021a;
Han et al., 2022; Qi et al., 2022b; Wang et al., 2022a; Xue et al., 2022;
Cheng et al., 2023] To provide specific examples, Wu et al. [2019a] developed
a user representation model that employs a neural architecture to enhance
recommendations from reviews. The model operates by first using a sentence-
level CNN to capture contextual information within sentences. It then applies
a sentence-level attention network to weigh the importance of different
sentences. Hu et al. [2020] implemented a news recommender system that adopts
a graph convolutional network for modeling long-term user interests. In
contrast, short-term user interests are captured using an attention-based LSTM
model. Another GNN-based recommender system, named Hierarchical User Intent
Graph Network, has been provided by Wei et al. [2022]. The core idea is to
leverage multi-level user intents and item representations to improve the
accuracy of recommendations. The framework is composed of three main
components: intra-level aggregation, inter-level aggregation, and interaction
prediction. The Core Attribute Evolution Network (CAEN), developed by Ma et
al. [2022b], incorporates a user behavior modeling method known as UB-GRUA
(User Behavior with Gated Recurrent Unit Attention), which is part of the
Personalized Attention Layer (PAL). This method is designed to capture and
interpret user interactions with items, particularly under the influence of
specific attributes. By employing a two-stage hierarchical attention network,
CAEN can discern the significance of different attributes to individual users,
leading to more accurate and personalized item recommendations.

Application domains covered include, among others, e-commerce (e.g., [Lin et
al., 2019; Gu et al., 2020; Ma et al., 2022b]), job recommendation (e.g.,
[Rimitha et al., 2018; Kota et al., 2021]), and news recommendation (e.g.,
[Natarajan and Moh, 2016; An et al., 2019; Wu et al., 2021a; Yang et al.,
2023]

####  4.4.2 Information retrieval

In the information retrieval context, user modeling research focuses
particularly on personalized search, which is a process that prioritizes a
user’s interests when searching through a set of documents or web pages. This
is achieved by creating a user profile based on the sites the user has
searched for, thereby identifying their interests. The accuracy of the system
is enhanced by predicting the user’s interests more accurately than a typical
search. In these personalized search systems, the search results are ranked
not only based on the query information but also according to the user’s
interests. This approach ensures a more tailored and relevant search
experience for the user. To cite specific cases, Zhou et al. [2020b] proposed
a personalized search model that aims to improve the re-finding behavior of
users when they search for previously encountered information. The model
leverages memory networks to capture two distinct types of re-finding
behavior: query-based and document-based. It utilizes Recurrent Neural
Networks (RNNs) to process sequential data and generate refined vectors that
represent user intent over multiple sessions. Zhou et al. [2021a] introduced a
group-based model that employs both search behavior and friend networks to
enhance search results for users, particularly those with sparse historical
data. By grouping users into friend circles and utilizing neural networks, the
model creates a more accurate semantic representation of user interests,
leading to significant improvements in search personalization. Deng et al.
[2022a] leveraged a dual-feedback network to enhance the understanding of user
search intentions. The model is designed to improve personalized search by
incorporating multi-granular user feedback, both positive and negative, to
accurately model the user’s current search intention. Other works can be seen
in Du et al. [2016], Ma et al. [2020], and Zhou et al. [2021b].

####  4.4.3 Adaptive e-learning systems

An adaptive e-learning system in the context of user modeling refers to an
educational technology platform that leverages user modeling techniques to
dynamically tailor the learning experience to the individual needs,
preferences, and abilities of each learner. In such a system, the emphasis is
on creating and updating a personalized user model that captures relevant
information about the learner, allowing the platform to adapt various aspects
of the e-learning process. Works in this area have always been proposed (e.g.,
[Brusilovsky, 2004; Brut et al., 2009; Li et al., 2009; Kim et al., 2013]).
Recently, Kulkarni et al. [2019] addressed the issue of delivering relevant
and high-quality content to users within e-learning systems, aiming to
minimize the time users spend searching for information. User profiles are
created by combining explicit information provided by the user with implicit
data gathered from their interactions with the system, resulting in a dynamic
and adaptive profile.

Along with ethical principles, there has also been an increasing recognition
of the imperative for accessibility in digital platforms, driven by a growing
awareness of the diverse needs of users and a commitment to ensuring inclusive
and equitable access to information and services. In this scenario, techniques
were also developed to generate user profiles that are tailored to the
specific accessibility needs of users with disabilities (e.g., [Sanchez-Gordon
et al., 2021]).

The last few years have witnessed a significant surge in the popularity of
Massive Open Online Courses (MOOCs) within the realm of e-learning platforms,
with the COVID-19 pandemic further accelerating their adoption as individuals
sought flexible and accessible online learning options. Sunar et al. [2020]
investigated the role of social engagement in MOOCs and its correlation with
course completion rates. The research specifically examines user behavior
aiming to model and understand the patterns of social interactions among
participants. The authors introduce the concept of “behavior chains” to model
the social engagement of learners. The study reveals that learners who engage
more deeply in peer interactions and contribute consistently over the duration
of the course are more likely to complete it. Jin [2023] focused, instead, on
the development of a predictive model to identify students at risk of dropping
out of MOOCs by analyzing their learning behavior data. The model employs
Support Vector Regression (SVR) as the core predictive algorithm due to its
suitability for the task. To enhance the performance of the SVR, the paper
introduces an Improved Quantum-behaved Particle Swarm Optimization (IQPSO)
algorithm for optimizing its parameters.

####  4.4.4 Fake news detection

The innovative application of user modeling has seen a notable rise in the
development of advanced approaches for detecting fake news, underscoring the
growing importance of leveraging personalized user profiles to enhance the
accuracy and effectiveness of misinformation detection in digital
environments. In the user modeling context, the majority of work analyzes
social media platforms and social networks (e.g., [Shu et al., 2018; Monti et
al., 2019; Shu et al., 2019]). A specific example is the paper by Sahoo and
Gupta [2021], where the authors presented a framework for detecting fake news
on Facebook by leveraging user profile features alongside news content
features. The system employs a combination of machine learning and deep
learning techniques to analyze the behavior of Facebook accounts. By
collecting data through a crawler and constructing datasets for
classification, the study utilizes user profile information as a critical
component in identifying fake news. Concerning ethical implications in AI
systems, particularly the risks of mislabeling news and unjust profiling,
Allein et al. [2023] proposed an ethical fake news detection approach that
incorporates user insights without relying on direct profiling, addressing the
challenge of profiling-dependent fake news detection. The proposed algorithm
is designed to leverage the social context of Twitter users during the
training phase but excludes user information during the evaluation of news
article veracity, thus avoiding decision-making based on profiling. The study
employs a multimodal learning algorithm that uses a cross-modal loss function
inspired by social sciences to analyze the relationship between news articles,
their creators, and the audience.

####  4.4.5 Cybersecurity

The innovative application of user modeling has witnessed a surge in the
integration of cybersecurity approaches, reflecting a heightened awareness of
the crucial role personalized user profiles play in enhancing digital security
and safeguarding against evolving cyber threats. Lashkari et al. [2019]
presented a broad examination of user profiling as a critical component in
cybersecurity, specifically for anomaly detection. The authors review existing
user profiling models, highlighting their strengths and weaknesses, and
propose a novel user profiling model designed to enhance the detection of
suspicious activities in cyberspace. Addae et al. [2019] investigated the
factors that influence users’ cybersecurity behaviors and the adoption of
personalized adaptive cybersecurity measures. The study integrates insights
from behavioral science, machine learning, and theoretical models such as the
Technology Acceptance Model (TAM) and Protection Motivation Theory (PMT) to
understand and predict user behavior in non-corporate environments.

Take-home messages from applications.  • Wide-ranging applications: User
modeling and profiling techniques are crucial in a variety of domains. While
initially integral to recommender systems and personalized platforms, these
techniques are now employed in a broader range of applications, including
adaptive e-learning systems, fake news detection, and cybersecurity. •
Adaptation to diverse data and purposes: These techniques involve extracting
and utilizing different types of data based on the specific purpose and method
of collection in each application.

##  5 Discussion and Future Research Directions

In this survey, we embarked on a comprehensive exploration of the evolving
landscape of user modeling and user profiling. Our journey commenced with a
thorough historical overview, tracing the roots of user modeling to provide a
contextual foundation for understanding its evolution. Subsequently, we
conducted an in-depth examination of existing surveys, critically analyzing
their contributions and consolidating diverse perspectives to glean insights
into the overarching developments. The analysis of terminology emerged as a
pivotal undertaking, revealing the semantic intricacies that permeate user
modeling and user profiling literature. By dissecting and categorizing the
terminological landscape, we aimed to foster a clearer understanding of the
field’s conceptual foundations, facilitating a more coherent discourse among
researchers and practitioners alike. As a culmination of this effort, we have
ventured to contribute to the field by providing two novel definitions that
encapsulate the essence of “user model” (or “user profile”) and “user
modeling” (or “user profiling”). A pivotal focus on paradigm shifts and new
trends unveiled the transformative forces shaping user modeling methodologies.
This section not only scrutinized historical transitions but also projected
forward, anticipating emerging trends that could redefine the landscape.
Concluding our exploration, we present the current taxonomy of user modeling,
synthesizing the diverse strands of research into a cohesive framework. This
taxonomy serves as a guidepost for researchers, offering a structured lens
through which to comprehend the evolving facets of user-centric modeling.

Despite the paradigm shifts that have characterized the evolution of user
modeling and profiling, certain core topics have exhibited remarkable
resilience, persisting as focal points throughout the discipline’s history.
The continual examination of users’ preferences, interests, and needs serves
as a foundational element, exemplifying an enduring pursuit to comprehend the
complexities of human behavior across diverse interactive contexts.
Statistical approaches, a traditional cornerstone of user modeling research,
have weathered the transformative waves of technological advancements,
highlighting their robustness in deciphering patterns within user data.
Moreover, the faithful applications of user modeling in recommender systems
and personalized, adaptive interfaces underscore the enduring relevance of
tailoring technological interactions to individual user characteristics. As we
navigate the currents of change, these enduring themes serve as anchors,
guiding and grounding user modeling research, ensuring continuity of inquiry
even in the face of evolving methodologies and technological landscapes.

With a future-oriented outlook, the principles of Human-Centered AI (HCAI)
[Shneiderman, 2022] and Responsible AI [Dignum, 2019], as well as the specific
regulations for the development of trustworthy AI systems, such as the EU AI
Act222https://www.europarl.europa.eu/RegData/etudes/BRIE/2021/698792/EPRS_BRI(2021)698792_EN.pdf,
are gaining widespread attention for their potential to shape the future of
technology in ways that serve human needs more effectively. These principles
emphasize designing AI systems that support human autonomy, enhance human
performance, and ensure that technology serves to empower people rather than
replace them. Building on these foundational concepts, research in user
modeling will probably undergo a significant transformation, highlighting the
importance of developing systems that are not only technologically advanced
but also deeply aligned with human values and needs. We can thus identify the
following emerging directions:

##### Human-AI Collaboration

The significance of human-AI collaboration is increasingly recognized as a
fundamental aspect of the future of research in user modeling, highlighting
the transformative potential of melding human intuition with AI’s analytical
prowess. Developing computational tools that act as interactive assistants
exemplifies this collaborative approach, where AI systems serve as partners in
the modeling process, aiding human modelers even in the absence of fully
specified outcomes. This symbiotic relationship enables a more adaptable and
responsive modeling process, attuned to the dynamic needs of users [Çelikok et
al., 2023]. Among recent contributions, the work of Gao et al. [2020] proposes
an explainable AI framework aimed at achieving human-like communication in
human-robot collaborations. By constructing a hierarchical mind model of the
human user, their approach allows for the generation of explanations based on
online Bayesian inference of the user’s mental state, significantly enhancing
collaboration performance and user perception of the robot. Similarly,
Banerjee et al. [2023] address the challenge of AI bots lacking
personalization and understanding of user intent by developing a system where
a human support agent collaborates with an AI agent to answer customer
queries, showcasing the potential of human-AI collaboration to enhance user
experience. These examples underscore the critical role of human-AI
collaboration in enhancing the adaptability, effectiveness, and ethical
grounding of user modeling systems. By fostering a collaborative dynamic
between humans and AI, the field of user modeling is set to advance towards
developing technologies that are not only sophisticated and efficient but also
deeply aligned with human values and needs.

##### Advanced User Models

Focusing on the development of user models that regard the user as an active
decision-maker engaged in a two-agent interaction with an AI collaborator
highlights the need for understanding not only the user’s goals but also their
cognitive computational capacity [Çelikok et al., 2023]. This perspective is
critical, as emphasized by Nasrudin et al. [2023], who underscore the
importance of models that accurately reflect the complex nature of human
behavior by considering user goals, cognitive capacities, and perceptions,
thereby impacting the sustainability of applications. Additional contributions
to this field include the work of Jégou and Chevaillier [2018], who propose a
computational model to facilitate turn-taking behaviors in user-agent
interactions, illustrating the significance of continuous decision-making
based on both the agent’s goals and the user’s intentions. Similarly, Humann
et al. [2023] present a graphical user interface tool that optimizes multi-
robot, multi-operator systems according to user preferences, serving as a
testament to the importance of tailoring computational models to accommodate
diverse user needs. These references collectively underscore the evolving
complexity and necessity of advanced user models that not only acknowledge the
user as a key decision-maker but also adapt to their unique cognitive
landscapes, thereby paving the way for more personalized and effective human-
AI collaborations.

##### Ethical and Equitable User Modeling

In line with Human-Centered AI (HCAI) principles, the ethical dimensions of
user modeling, including data sovereignty and privacy, hold significant
importance. Nigel Shadbolt [2020] discusses methodologies for ensuring data
privacy and fostering individual autonomy within digital ecosystems,
emphasizing a critical aspect of responsible AI development. This focus on
ethical considerations is essential in the development and deployment of user
modeling systems, ensuring that they align with broader societal values and
individual rights. Further exploration into the ethical dimensions of user
modeling reveals a growing body of research dedicated to addressing these
concerns. For instance, work by Shahar [2021] on advanced user models in
shared medical decision-making showcases the potential to address human
cognitive limitations while ensuring ethical decision-making, highlighting the
balance between AI assistance and human autonomy. By focusing on privacy,
autonomy, and transparency, the field can advance toward developing
technologies that are advanced and efficient, as well as ethically grounded
and aligned with human values and societal norms.

##### Integration of Cognitive Sciences

Combining insights from cognitive sciences, psychology, and human-computer
interaction, an interdisciplinary approach to user modeling is essential for
developing more advanced and accurate user models [Çelikok et al., 2023]. This
integration offers significant potential to enhance our understanding of user
behavior, informing the development of systems that can adapt to human actions
in a nuanced and personalized manner. Streicher and Bauer [2024]’s application
of a Bayesian cognitive state modeling approach to adaptive educational
serious games showcases how cognitive modeling can dynamically adapt in
serious gaming environments, illustrating the impact of cognitive insights on
user model effectiveness. Madsen et al. [2019] discuss the use of agent-based
models in cognitive sciences for predicting behaviors in dynamic, adaptive,
and heterogeneous agents, highlighting the utility of agent-based models in
scaling cognitive models within social networks and validating model
predictions. This work serves as a crucial link between individual and
socially oriented models. Incorporating cognitive principles and methodologies
allows researchers and developers to create user models that are not only more
aligned with human thought processes and behaviors but also capable of
fostering more engaging and effective human-machine interactions. This
interdisciplinary approach is foundational for creating systems that truly
understand and adapt to user needs in a personalized manner.

##### AI Systems Understanding User Goals

Creating AI systems capable of understanding and adapting to user goals is
pivotal for the advancement of responsive and supportive technologies [Çelikok
et al., 2023]. This notion is reinforced by Zeng [2021]’s exploration into
human-centered intelligent gaming systems, which utilize machine learning
algorithms to enhance player experiences by personalizing gameplay and
understanding player motivations, underscores the significance of adapting AI
systems to user goals. This approach not only enhances user engagement but
also fosters the development of human-like characters and adaptive recommender
systems, marking a step forward in creating more immersive and personalized
interactive environments. Moreover, the study by Virós-i Martin and Selva
[2022] on AI assistants that adapt to designers’ learning goals during design
space exploration illustrates the potential benefits of AI systems that adjust
to user preferences and objectives. Their research highlights the importance
of AI systems in improving users’ understanding of complex tasks, even though
it may affect task performance due to changes in user interaction patterns.
Collectively, these insights underline the critical role of AI systems
designed to understand and adapt to user goals. Such systems not only promise
to revolutionize how we interact with technology but also pave the way for
more personalized, intuitive, and effective human-machine collaborations.

##### Cross-Modal User Interaction

Cross-modal user interaction has seen notable advancements, particularly in
enhancing user experiences by bridging diverse modalities. The development of
a method for user-generalized cross-modal retrieval is a prime example of this
progress, providing significant improvements in user experience across
different interaction modes [Ma et al., 2022c]. This advancement emphasizes
the evolving landscape of user modeling research, where understanding and
catering to the multifaceted needs and preferences of users through diverse
sensory modalities are becoming increasingly important. In the educational
domain, implementing a cross-modal UX course for industrial design students,
incorporating auditory and haptic techniques, aims to create inclusive and
accessible user experiences. This initiative highlights the potential of
cross-modal approaches in fostering a more comprehensive understanding of user
interaction within user modeling research [Temor et al., 2022]. Moreover, the
exploration of “cued-gaze” with voice agents by Jaber and McMillan [2022]
reveals that integrating visual cues with auditory instructions through cross-
modality repair is more effective than speech reformulation alone. This
finding emphasizes the importance of multimodal interactions in advancing user
modeling, suggesting that user experiences can be significantly enriched by
understanding and leveraging the interplay between different sensory
modalities. These advancements in Cross-Modal User Interaction directly
contribute to the field of user modeling by offering new insights into how
users interact with and perceive multimodal systems. By integrating diverse
modalities, researchers can develop more nuanced and comprehensive models of
user behavior, preferences, and needs. This opens new avenues for designing
more accessible, intuitive, and engaging human-computer interfaces,
accentuating the symbiotic relationship between cross-modal interaction and
user modeling research.

Consideration. These novel research directions in user modeling, underpinned
by the principles of Human-Centered AI and Responsible AI, represent a
significant shift towards developing technologies that are not only advanced
but also deeply aligned with human values, needs, and ethical considerations.
By focusing on these areas, the field of user modeling is poised to contribute
to the creation of more personalized, efficient, and intuitive interactions
between humans and technology, ultimately fostering a more humane and
sustainable future.

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