# NEON: Living Needs Prediction System in Meituan

Xiaochong Lan  Department of Electronic Engineering, BNRist,Tsinghua
UniversityBeijingChina<EMAIL_ADDRESS>,  Chen Gao${\dagger}$
Department of Electronic Engineering, BNRist,Tsinghua UniversityBeijingChina
<EMAIL_ADDRESS>,  Shiqi Wen  MeituanBeijingChina<EMAIL_ADDRESS>,
Xiuqi Chen  MeituanBeijingChina<EMAIL_ADDRESS>,  Yingge Che
MeituanBeijingChina<EMAIL_ADDRESS>,  Han Zhang  MeituanBeijingChina
<EMAIL_ADDRESS>,  Huazhou Wei  MeituanBeijingChina
<EMAIL_ADDRESS>,  Hengliang Luo  MeituanBeijingChina
<EMAIL_ADDRESS>and  Yong Li  Department of Electronic Engineering,
BNRist,Tsinghua UniversityBeijingChina<EMAIL_ADDRESS>
(2023)

###### Abstract.

††footnotetext: ${\dagger}$Chen Gao is the corresponding author
(chgao96@gmail.com).

Living needs refer to the various needs in human’s daily lives for survival
and well-being, including food, housing, entertainment, etc. At life service
platforms that connect users to service providers, such as Meituan, the
problem of living needs prediction is fundamental as it helps understand users
and boost various downstream applications such as personalized recommendation.
However, the problem has not been well explored and is faced with two critical
challenges. First, the needs are naturally connected to specific locations and
times, suffering from complex impacts from the spatiotemporal context. Second,
there is a significant gap between users’ actual living needs and their
historical records on the platform. To address these two challenges, we design
a system of living NEeds predictiON named NEON, consisting of three phases:
feature mining, feature fusion and multi-task prediction. In the feature
mining phase, we carefully extract individual-level user features for
spatiotemporal modeling, and aggregated-level behavioral features for
enriching data, which serve as the basis for addressing two challenges,
respectively. Further, in the feature fusion phase, we propose a neural
network that effectively fuses two parts of features into the user
representation. Moreover, we design a multitask prediction phase, where the
auxiliary task of needs-meeting way prediction can enhance the modeling of
spatiotemporal context. Extensive offline evaluations verify that our NEON
system can effectively predict users’ living needs. Furthermore, we deploy
NEON into Meituan’s algorithm engine and evaluate how it enhances the three
downstream prediction applications, via large-scale online A/B testing. As a
representative result, deploying our system leads to a 1.886% increase w.r.t.
CTCVR in Meituan homepage recommendation. The results demonstrate NEON’s
effectiveness in predicting fine-grained user needs, needs-meeting way, and
potential needs, highlighting the immense application value of NEON.

Living Needs Prediction; Deep Neural Networks; Multi-task Learning

††journalyear: 2023††copyright: rightsretained††conference: Proceedings of the
29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; August
6–10, 2023; Long Beach, CA, USA††booktitle: Proceedings of the 29th ACM SIGKDD
Conference on Knowledge Discovery and Data Mining (KDD ’23), August 6–10,
2023, Long Beach, CA, USA††doi: 10.1145/3580305.3599874††isbn:
979-8-4007-0103-0/23/08††ccs: Information systems Information systems
applications

##  1\. Introduction

Living needs are the various needs generated by individuals in their daily
routines for daily survival and well-being. Typical living needs include
necessities such as food, housing, and personal care as well as leisure
activities such as entertainment, for which there exists a diverse array of
life service providers. Meituan††https://www.metiuan.com is a large platform
connecting customers to life service providers, in which users can meet almost
all kinds of living needs. Unlike traditional information systems such as
e-commerce websites, where users can only purchase products (i.e., meeting one
kind of living needs), Meituan allows access to various living services, such
as booking a hotel, ordering takeout , etc. Moreover, an accurate
understanding of users’ needs can significantly improve user experience, for
example, by enhancing the downstream recommendation tasks. Generally speaking,
generating a specific kind of living need is naturally accompanied by a
specific time or location, such as ordering food delivery at the office. Thus,
the problem of living needs prediction can be defined as predicting the type
of needs of the target user’s spatiotemporal context, as illustrated in Figure
1.

Figure 1. Living needs prediction aims to predict the specific living need of
a user given the spatiotemporal context. By predicting users’ living needs,
life service platforms can recommend life services that can fulfill these
needs.

For this new problem from the real-world scenario, there are two closely-
related research topics, demand forecasting (Lasek et al., 2016; Seyedan and
Mafakheri, 2020; Ren et al., 2020; Song et al., 2019; Hernandez et al., 2014)
and spatiotemporal activity prediction (Fan et al., 2019; Zheng et al., 2010;
Bhargava et al., 2015; Kipf and Welling, 2016; Li et al., 2022). Demand
forecasting is dedicated to predicting the quantity of a product or service
that consumers will purchase. However, these works focus on the problem of
aggregated demand prediction with times-series modeling, ignoring the needs of
the specific user, time, and location. As for spatiotemporal activity
prediction, the existing works focus mainly on either online activities such
as App usage, or offline activities such as location visitation, while
individuals’ living needs can be fulfilled both in-store (offline) and via
delivery (online), leading to a completely new problem.

There exist two critical challenges for living needs prediction as follows.

  * •

The impact of spatiotemporal context is complex. As discussed above, for the
same user, living needs at different locations or times are totally different.
For example, the user in Figure 1 eats food delivery at noon and watches a
movie at night. Additionally, the lifestyles, i.e., the spatiotemporal pattern
of living needs, are extremely various for different users, further making it
more difficult to model the spatiotemporal context.

  * •

There is a significant gap between users’ actual living needs and their
historical records on the platform.  Typically, users will face various kinds
of situation in their life, and will generate multiple living needs. But they
may only choose to satisfy one or a few of them on the platform, leading to a
significant gap between their actual living needs and their historical
records. We refer to the needs that can not be observed from historical
records as potential needs. The case here is different from that in most
recommender systems, where the actual interests and the historical record
generally do not differ greatly for users, leading to another critical
challenge.

To address these challenges, in this work, we described our deployed NEON
system (short for living NEeds predictiON) in Meituan, which includes three
phases: feature mining, feature fusion, and multitask prediction. First, in
the feature mining phase, to address the first challenge, we carefully design
the spatial and temporal features for individual-level users, and to address
the second challenge, we extract the behavioral-pattern features for group-
level users. Second, in the feature fusion phase, we develop a feature-fusion
neural network that combines internal preferences, impact from spatiotemporal
context, and group behavior patterns to generate user representations,
addressing both challenges. Last, as the complement to the main task of living
needs prediction, we introduce the auxiliary task of needs-meeting way
prediction to enhance the model’s learning of spatiotemporal context, further
addressing the first challenge.

The proposed NEON system plays a critical role in Meituan’s recommendation
engine with various downstream applications, including homepage
recommendation, Guess-you-like page recommendation, and message pop-up
recommendation, which requires the different-aspect ability of living needs
prediction. After the deployment of the proposed system, we obtain stable and
significant gains in three applications, providing strong real-world evidence
for NEON’s effectiveness from different perspectives.

The contribution of this work can be summarized as follows.

  * •

To the best of our knowledge, we take the first step to study the problem of
living needs prediction, which is a critical problem in real-world life
service platforms but has not been well explored.

  * •

We proposed the NEON system, which includes three phases of feature mining,
feature fusion layer, and multitask prediction, which well addresses the two
challenges, the complex impact of spatiotemporal context and missing
behavioral data.

  * •

We deploy NEON in Meituan’s recommendation engine and conduct large-scale
online A/B tests on three typical downstream applications, along with
extensive offline evaluations. Offline experimental results verify that NEON
can accurately predict users’ living needs. The downstream evaluations
strongly confirm NEON’s high application value, with significant performance
improvement in three downstream applications, among which a representative
result is a 1.886% increase w.r.t. CTCVR for Meituan homepage recommendation.

##  2\. Problem Statement

As we discussed above, in users’ daily life, they generate various living
needs, such as eating, accommodation, entertainment, beauty, etc. These needs
can be fulfilled by life service providers in the city, which can be accessed
through platforms connecting life service providers to customers. To enhance
the user experience, it’s crucial for these platforms to accurately predict
users’ needs and recommend appropriate services. This leads to the problem of
living needs prediction.

As defined in the introduction, living needs prediction aims to predict the
specific living needs of a user given the spatiotemporal context in which they
are located (in the following we also refer to this as given the user scene).
To clearly define the problem, with the help of experts, we divide all the
living needs that users can satisfy on the platform into ten categories, shown
in Table 1. We use $\mathcal{N}$ as the symbol for the set of all living
needs. In response to these needs, all the life services on Meituan are also
divided into 10 categories. The problem can be defined as follows.

Table 1. 10 types of living needs that can be satisfied in Meituan | Ordering food delivery, Eating in a restaurant,
---|---
Living | Booking a hotel, Buying medicine,
Needs | Specialty shopping online, Hair-dressing,
| Grocery shopping online, Beauty,
| Tourism and Entertainment

Input: A dataset $\mathcal{O}^{+}$ of real-world life service consumption
records that reflect users’ living needs. Each instance in the dataset tells
the kind of life service a specific user purchases, which indicates the
specific living need $n$ ($n\in\mathcal{N}$) of the user in a specific user
scene $i$.

Output: A model to estimate the probability that a user will generate the
living need $n$ in user scene $i$, formulated as $f(i,n|\mathcal{O}^{+})$.
Here $f(\cdot)$ denotes the function that the model aims to learn.

##  3\. Our NEON System

To address the challenges mentioned in the introduction, we develop the NEON
system made up of three phases: feature mining, feature fusion layer, and
multitask prediction. First, in the feature mining phase, we address the first
challenge by carefully designing spatiotemporal features for individual-level
users and address the second challenge by extracting behavioral-pattern
features for group-level users. The feature fusion phase then employs a
feature-fusion neural network to seamlessly integrate internal preferences,
spatiotemporal context impact, and group behavior patterns to generate
complete user representations, overcoming both challenges. Last, in the
multitask prediction stage, to enhance the model’s understanding of
spatiotemporal context, we introduce an auxiliary task of needs-meeting way
prediction to the main goal of predicting living needs, providing additional
support in addressing the first challenge. The deep feature fusion layer and
the multi-task prediction parts of our system are illustrated in Figure 2.

Figure 2. Illustration of living needs prediction system NEON.

###  3.1. Feature Mining

First of all, we use features that directly reflect user traits, such as
users’ profile, their recent behavior sequence, and their historical
behaviors, as inputs for the model.

As mentioned above, for a specific user, his living needs are greatly affected
by the spatial and temporal scenarios in which he is located. For example, on
a rainy midday, a person at work probably has the need to order food delivery;
but on a sunny noon, he/she may have another need of going out to eat in the
restaurant. This complexity and variability of human living needs, driven by
the flux of time and space, pose a considerable challenge in accurately
modeling the impact of the spatiotemporal context. In order to tackle this
challenge, we incorporate spatiotemporal context features as an integral part
of our system’s input.

What’s more, on the platform, users may have potential needs with sparse or
even non-existent history records. For example, a person who never buys
medicine on the platform may have a cold and need to buy medicine online one
day. Such potential needs are difficult for the model to grasp. To address the
challenge of modeling potential needs, we introduce group behavior pattern
features to help the model learn the potential living needs of users.

Below we give a detailed description of the three categories of features.

####  3.1.1. User Features

This group of features includes user profiles and user history behavior
sequences.

  * •

User profiles $\bm{f}^{U}_{p}$. The user’s profile, including their age,
gender, etc.

  * •

User recent online behavior sequence $\bm{f}^{U}_{rb}$. The sequence of items
recently clicked by the user in the platform; the sequence of items recently
ordered by the user in the platform.

  * •

User aggregated historical online behavior $\bm{f}^{U}_{hb}$. The percentage
of times users buy each type of life service.

  * •

User offline visitation record $\bm{f}^{U}_{ov}$. The 50 most visited POIs
(point of interest) by the user in the last six months; the 50 most visited
AOIs (area of interest) by the user in the last six months.

We concatenate all the mentioned features above to get a sparse user feature
vector $f^{U}$, formulated as follows:

(1) $f^{U}=\left[f^{U}_{p},f^{U}_{rb},f^{U}_{hb},f^{U}_{ov}\right].$

####  3.1.2. Spatiotemporal Context Features

Users’ living needs are greatly affected by time, location, and other
environmental factors. Thus, we introduce spatiotemporal context features as
part of the input of our system to help our system model the complex impact of
spatiotemporal context, which can be listed as follows.

  * •

Time $\bm{f}^{ST}_{t}$. Current time period. More than one time period feature
of different granularity is applied, including hour, day, whether it is a
holiday, etc.

  * •

Location $\bm{f}^{ST}_{l}$. The POI (point of interest) embedding of the
user’s real-time location; the AOI (area of interest) embedding of the user’s
real-time location; the city embedding of the user’s real-time location. The
location features are hourly real-time features.

  * •

Weather $\bm{f}^{ST}_{w}$. Weather information for the user’s city or region,
including wind, humidity, temperature, and weather type (sunny, rainy, snowy,
etc.). Weather features are refined to hourly granularity.

  * •

Travel state $\bm{f}^{ST}_{ts}$. Information about whether the user is located
in his/her resident city. Possible states include based in resident city,
about to travel, and on travel.

The dense spatiotemporal context feature vector $f^{ST}$ is created by
concatenating all previously mentioned context features, formulated as
follows:

(2) $f^{ST}=\left[f^{ST}_{t},f^{ST}_{l},f^{ST}_{w},f^{ST}_{ts}\right].$

####  3.1.3. Group Behavior Pattern Features

We introduce group behavior pattern features to supplement the sparse
individual behavior of users, in order to assist in identifying the potential
living needs of individual users.

  * •

Group aggregated behavior $\bm{f}^{G}_{a}$. We first segment users into groups
based on their profiles. In each group, we get the group aggregated behavior
by calculating the percentage of views, clicks, and purchases of each type of
life service among all views, clicks, and purchases initiated by the group.
For each user, the group aggregated behaviors of the groups the user belongs
to are used as features. For example, a middle-aged person has group
aggregated behaviors feature of middle-aged users and other groups he/she is
in.

  * •

Popularity in the current time period $\bm{f}^{G}_{ct}$. We cut all time into
time periods according to different criteria, such as whether it is a holiday,
if it is morning, noon, or night, etc. Then in each time period, we calculate
the popularity of each type of life service by calculating the percentage of
times the life service is viewed/clicked/purchased among all
views/clicks/purchases happening in this time period. We determine the time
periods in which the current time is located, and use the popularity of each
type of life service in these time periods as a feature. For example, if the
user opens the app on Christmas night, popularity on holiday and popularity at
night of each kind of life service are set as features.

  * •

Group behavior pattern in spatiotemporal context $\bm{f}^{G}_{st}$. By
discovering group preferences in different spatiotemporal contexts, we further
capture more fine-grained group behavior patterns. We calculate the percentage
of views/clicks/purchases of each type of life service initiated by each group
in each kind of spatiotemporal context. These fine-grained patterns are used
as features of the model. For example, the group preference of middle-aged
people at work at noon on working days are used to enrich the representations
of every individual within this demographic in such spatiotemporal scenario.

  * •

User behaviors augmented by inter-need correlation $\bm{f}^{G}_{ic}$. There is
an inherent association across different types of users’ living needs. This
association can be leveraged to improve prediction performance. For example, a
user who frequently purchases hairdressing services may also be inclined to
purchase beauty services. We use the association rule mining algorithm to
analyze the co-occurrence of different life service categories, filter out
high-correlation relationships, and employ them to augment user behavior as
input features.

We combine all previously mentioned group behavior pattern features to
generate the dense group behavior pattern feature vector $f^{G}$, which is
formulated as follows:

(3) $f^{G}=\left[f^{G}_{a},f^{G}_{ct},f^{G}_{st},f^{G}_{ic}\right].$

###  3.2. Feature Fusion Layer

As mentioned in Section 2, we refer to a user in a specific spatiotemporal
context as a user scene. For a user scene $i$, after feature mining, we have
dense spatiotemporal features $f^{ST}_{i}$, dense group pattern features
$f^{G}_{i}$, and sparse user features $f^{U}_{i}$. For brevity of
presentation, we omit the subscript $i$ in some of the expressions below. We
designed a feature fusion layer to integrate these features into the input of
the subsequent prediction module.

We first set up an embedding layer, which processes the high-dimensional
sparse user feature vector $f^{U}$ into a low-dimensional dense vector
$v^{U}$. To address the challenges of complex impact of spatiotemporal context
and users’ potential needs, we mine spatiotemporal features and group behavior
pattern features in the feature mining phase, respectively. With these
features as input, we use a feature merging network to model the interaction
between spatiotemporal contexts, group behavior patterns, and users as
follows,

(4) $x^{M}=h^{M}\left(\left[f^{ST},f^{G},v^{U}\right]\right),$

where $\left[\cdot\right]$ denotes concatenation operation. Here $h^{M}$ is
the feature merging network, which merges three information sources of
spatiotemporal contexts, group behavior patterns, and user preference into a
fusion representation $x^{M}$.

Moreover, users have their own internal characteristics that are independent
of the spatiotemporal scene they are in and the group they belong to. To model
the internal characteristics of users, we generate a representation as
follows,

(5) $x^{U}=h^{U}\left(f^{U}\right),$

where $h^{U}$ denotes the user preference network that turns raw user features
into dense user preference representation. We then concatenate the two parts
of representations into the full representation of the user scene:

(6) $x=\left[x^{M},x^{U}\right].$

In brief, we design a feature fusion layer to tackle both challenges by
considering the influence of spatiotemporal context, incorporating group
behavior patterns, as well as extracting individual preferences.

###  3.3. Multitask Prediction

We further design a prediction module which takes user scene representation as
input to predict users’ living needs. The module is tasked with two
objectives: fine-grained need prediction and needs-meeting way prediction.
Fine-grained need prediction is to predict the specific living need of the
user. Neets-meeting way prediction is to predict the preferred way of the user
to meet their needs.

Specifically, among the ten kinds of needs which we mentioned in the problem
formulation, there are two ways to satisfy the needs: in-store and via-
delivery. In other words, consumers can choose to satisfy their needs by
visiting a physical store or by ordering online and then receiving goods via
delivery. Each type of the 10 needs can be classified into one of two
categories, in-store needs or via-delivery needs. We show the classification
in Table 2. Actually, needs-meeting way prediction is to predict whether the
preferred way of the user to meet their needs is in-store or via-delivery.

Table 2. The classification of the 10 living needs that can be satisfied on Meituan Living needs that | Specialty shopping online,
---|---
can be satisfied | Grocecy shopping online,
via delivery | Ordering food delivery, Buying medicine
Living needs that | Eating in a restaurant, Hotel,
can be satisfied | Hair-dressing, Beauty,
in store | Tourism, Entertainment

Users’ preferences for needs-meeting ways are strongly affected by the
spatiotemporal context. For example, a person at work during lunchtime on a
weekday is more likely to have the need to order food delivery (via delivery),
while the same person in a shopping district on a weekend evening is more
likely to visit a store for a meal (in-store). With this in mind, we include
the needs-meeting way prediction task which is jointed trained with the main
task of need prediction to enhance the model’s ability to learn spatiotemporal
context information. Next, we describe how we get the prediction results of
the two tasks. We use $y^{W}$ and $y^{N}$ to denote the prediction result of
needs-meeting way and specific need. $y^{k},k\in\\{W,N\\}$ can be generated as
follows,

(7) $\displaystyle y^{k}$ $\displaystyle=t^{k}(z^{k}),$
$\displaystyle\text{where }z^{k}$
$\displaystyle=g^{k}(x)_{0}E^{k}(x)+g^{k}(x)_{1}E^{S}(x).$

Here $t^{k}$ is the prediction neural network for task $k$. There are a
variety of choices in the specific structure of the neural network. In Section
A.1, we will state our specific choice. To avoid verbosity, we will use
network to replace neural network in the following text. The output of
$t^{N}$, $y^{N}$, is the scores of ten types of living needs, and the output
of $t^{W}$, $y^{W}$, is the scores of in-store and via-delivery needs-meeting
ways. We use $s^{N}_{im}$ to denote the score of need $m$ for user scene $i$,
and use $s^{W}_{in}$ to denote the score of needs-meeting way $n$ for user
scene $i$. $E^{k}$ is the expert network (Ma et al., 2018b; Tang et al., 2020)
for task $k$. $E^{S}$ is the shared network between the two tasks. $E^{S}$ is
responsible for generating general representations that are common to both
tasks, while $E^{k}$ is responsible for learning task-specific representations
that are more fine-tuned to the specific task $k$. The gating network $g_{k}$
determines what proportion of information input each task’s prediction network
receives from the shared network and the expert network. We formulate the
gating network as follows,

(8) $g^{k}(x)=\operatorname{Softmax}(W_{k}x),$

where $W_{k}\in\mathbb{R}^{2\times d}$ is trainable weights for task $k$. The
gating network takes $x$ as input, and outputs the relative importance of the
shared and task-specific representations for a given tasking, allowing the
model to selectively attend to the most relevant information and improve its
performance. In summary, to address the complexity of spatiotemporal context
impact, we introduce an auxiliary task of needs-meeting way prediction which
is jointly trained with the main task of fine-grained living needs prediction
to enhance our system’s learning of spatiotemporal context. The multitask
prediction module in our system produces a score for each living need and
needs-meeting way.

###  3.4. Model Training

In this section we describe how our system is trained. Corresponding to the
two tasks, we design two parts of loss. We design need prediction loss taking
into account the fact that the frequency of different needs arising in users’
lives is different. For example, a user may need to order food delivery for
lunch every workday, but rarely need to buy medicine. In order to address the
class imbalance issue for different living needs, we propose using a multi-
class focal loss which can decrease the effect of needs with a high volume of
training data on the final prediction loss. The need prediction loss can be
formulated as follows,

(9) $\displaystyle\text{Loss}_{\text{need}}$ $\displaystyle=-\sum_{i\in
O}\left(\sum_{n=1}^{10}\left(1-q^{N}_{in}\right)^{\gamma}\chi^{N}_{in}\log\left(q^{N}_{in}\right)\right),$
(10) $\displaystyle\text{where }q^{N}_{in}$
$\displaystyle=\operatorname{Softmax}\left(s^{N}_{in}\right)=\frac{e^{s^{N}_{in}}}{\sum_{n}e^{s^{N}_{in}}}.$

Here $O$ is the training set, $s^{N}_{in}$ is the score of living need $n$ for
user scene $i$, $\gamma$ is the hyperparameter which decides the importance of
difficult samples, $\chi^{N}_{in}$ is 1 if $n$ is the ground truth need for
user scene $i$, else it is 0. For the needs-meeting way prediction task, we
use BCE loss as prediction loss. We formulate it as follows,

(11) $\displaystyle\text{Loss}_{\text{way}}$ $\displaystyle=-\sum_{i\in
O}\left(\sum_{m=1}^{2}\chi^{W}_{im}\log\left(q^{W}_{im})\right)\right),$ (12)
$\displaystyle\text{where }q^{W}_{im}$
$\displaystyle=\operatorname{Softmax}\left(s^{W}_{im}\right)=\frac{e^{s^{W}_{im}}}{\sum_{m}e^{s^{W}_{im}}}.$

Here $O$ is the training set, $s^{W}_{im}$ is the score of needs-meeting way
$m$ (in store or via delivery )for user scene $i$. $\chi^{W}_{im}$ is 1 if $m$
is the ground truth needs-meeting way for user scene $i$, else it is 0. In our
system, the feature integration module and multitask prediction module are
trained end to end. The entire loss function is:

(13)
$\text{Loss}=\lambda_{1}\text{Loss}_{\text{need}}+\lambda_{2}\text{Loss}_{\text{way}}.$

$\lambda_{1}$ and $\lambda_{2}$ are hyperparameters that control the
importance of the two parts of loss.

##  4\. Offline Evaluation

###  4.1. Experimental Settings

####  4.1.1. Dataset

We conduct an offline experiment on a real-world dataset at a billion-scale.
The dataset comprises a sampling of all 2022 purchase records on the platform,
based on the percentage of purchases for each type of life service. It
consists of over 7 billion actual purchase records from 65 million users. The
details of datasets are provided in Appendix A.2.

####  4.1.2. Metrics

We design three metrics, namely Sort Accuracy (SA), Via-delivery Sort Accuracy
(VDSA), and In-store Sort Accuracy (ISSA), to measure the performance of our
system and baseline systems in predicting living needs. SA measures the
overall accuracy of sorting living needs based on their scores. VDSA focuses
on the accuracy of predicting needs that are satisfied via delivery, while
ISSA focuses on the accuracy of predicting needs for in-store scenarios.
Detailed definitions of these metrics are provided in Appendix A.3.

####  4.1.3. Baselines

To illustrate the effectiveness of our system, we compare it with two
baselines widely in actual production environments, including DIN (Zhou et
al., 2018), DNN (Cheng et al., 2016), DCN (Wang et al., 2017), ESMM (Ma et
al., 2018a), and MMOE (Ma et al., 2018b). We will provide a detailed
description of these baselines in Appendix A.4.

###  4.2. Overall Performance

We test the performance of our proposed system and baselines on the living
needs prediction task, and show the results in Table 3. We can have the
following observations.

Table 3. Offline experimental performance of NEON and baselines. Method | VDSA | ISSA | SA
---|---|---|---
DIN | 0.9044 | 0.7467 | 0.8700
DNN | 0.9060 | 0.7500 | 0.8718
DCN | 0.9051 | 0.7466 | 0.8701
ESMM | 0.9080 | 0.7476 | 0.8708
MMOE | 0.9097 | 0.7573 | 0.8757
NEON | 0.9175 | 0.8277 | 0.9070
Improvement | 0.86% | 9.30% | 3.57%
Table 4. Performance of NEON with/without multitask prediction. | VDSA | ISSA | SA
---|---|---|---
w.o. | 0.9089 | 0.8084 | 0.8968
with | 0.9175 | 0.8277 | 0.9070
Improvement | 0.95% | 2.39% | 1.14%

  * •

Our system steadily outperforms all baselines on all metrics. The improvement
of our system compared to the best baseline is 0.86%, 9.30%, and 3.57% w.r.t.
VDSA, ISSA, and SA, respectively. The significant performance gain confirms
the effectiveness of our system on the living needs prediction task.
Furthermore, such a significant improvement in the ability to predict living
needs will result in a huge benefit in real-world production scenarios, which
will be further confirmed through online evaluation.

  * •

Our system achieves greater improvement on ISSA. The task of predicting users’
in-store living needs is relatively difficult, since the in-store consumption
data is sparser, and the relationships between spatiotemporal context and in-
store needs are more complex. On this task, all methods perform the worst, and
our system outperforms baselines on a large margin, with an improvement of
9.30% w.r.t. ISSA. This further confirms our model’s ability to tackle the
complex impact of spatiotemporal context and discovering potential needs.

###  4.3. Ablation Study

As mentioned in Section 3.3, we introduce the task of needs-meeting way
prediction to enhance the system’s learning of spatiotemporal context. To
study the effectiveness of the multitask prediction design, we remove it from
our system to observe the impact of the design on the system performance.
Specifically, we change the system structure by removing the needs-meeting way
prediction network $t^{W}$ and taking the sum of $z^{N}$ and $z^{W}$ as the
input of the need prediction network $t^{N}$, and test the performance of the
changed system. The results are shown in Table 4. The results show that our
proposed system outperforms the system without multitask prediction design.
Our system performs better w.r.t. VDSA by 1.16%, w.r.t. ISSA by 2.39%, and
w.r.t. SA by 1.14%. The significant performance improvement confirms the
validity of the multitask prediction design.

##  5\. Online Evaluation

Figure 3. Online deployment of NEON in Meituan Recommendation Engine. In
summary, NEON helps to generate the quotas of categories of life services in
the recommendation list (homepage recommendation and guess-you-like
recommendation), or decide the one category to be recommended to the user
(message pop-up recommendation).

For life service platforms such as Meituan, understanding and predicting
users’ living needs are important in all business scenarios. In this section,
we evaluate the performance of three downstream recommendation tasks when
deploying NEON into Meituan’s recommender engine, and the illustration of
deployment is shown in Figure 3. Specifically, the three typical applications
are homepage recommendation, Guess-you-like recommendation, and message pop-up
recommendation. The performance of NEON on these applications reflects its
effectiveness on fine-grained need prediction, needs-meeting way prediction,
and potential need prediction, respectively. We elaborate on the online
testing of three tasks one by one as follows.

###  5.1. Homepage Recommendation

In this section, we evaluate the performance of NEON when deployed to homepage
recommendation, which reflects its ability on fine-grained living need
prediction.

####  5.1.1. Deployment Scenario

Typically, users open Meituan mobile APP to meet their certain living need. In
the overall recommendation list on homepage offered by Meituan, users probably
only focus on the items which belong to the type of life service that can
fulfill their living needs, and choose one item from the items. Whether an
item is in the category of life service that the user need is at least as
important as whether the user likes the item. To emphasize the importance of
recommending needed items, the engine follows a two-step approach to generate
the final recommendation list from the pre-ranking result. For a user scene,
it first decides the quotas for all the ten categories of life services and
then generates the final list according to the quotas and the prediction
scores of the items. The supply quotas are generated based on the scores our
system outputs along with other criteria.

In short, our NEON system is used to generate the quotas of different kinds of
life services in the homepage recommendation list.

####  5.1.2. Experiment Setting

We will first give a detailed description of how our system is used in
generating the quotas for each category of life services. At first, we recall
local life service items into the recall pool using various strategies, such
as popularity and collaborative filtering. Then the engine outputs a
preliminary recommendation list based on the recall pool, called pre-ranking
list. After that, we take the Softmax normalized scores output by our system
as the proportional quotas for each category of life service. We further
adjust the quotas taking the proportion of each category in the pre-ranking
list, supply distribution by category, and order distribution by category into
account. The generated quotas are the proportion of each category in the lists
received by users. The lists are generated considering both quotas of
categories and prediction scores of items.

We compare the homepage recommendation performance of our whole recommendation
engine with and without our system through online A/B tests. The tests last
for one week and involved around 4.5 million users.

In our tests, the users are randomly divided into two buckets of similar size
and assigned different methods for calculating quotas for each category.
Specifically, for the first group, we use the method described previously,
while for the second group, we generate quotas based only on the proportion of
each category in the pre-ranking list, supply distribution by category, and
order distribution by category. We maintain consistency across all other
modules to ensure a fair comparison.

For metrics, we use Click Through Rate (CTR), Conversion Rate (CVR), Click to
Conversion Rate (CTCVR) to measure the quality of the final recommendation
list, which are widely-used measurements (Ma et al., 2018a; Wen et al., 2020;
Liu et al., 2020).

Table 5. Results of A/B tests on homepage recommendation Metric | w/o NEON | with NEON | Improvement
---|---|---|---
CTR | 3.0601$\times 10^{-2}$ | 3.0672$\times 10^{-2}$ | +0.230%
CVR | 1.1497$\times 10^{-1}$ | 1.1687$\times 10^{-1}$ | +1.652%
CTCVR | 3.5183$\times 10^{-3}$ | 3.5846$\times 10^{-3}$ | +1.886%

####  5.1.3. Performance

The results of our A/B tests are shown in Table 5. From the results, we can
have the following observations:

  * •

There is a significant improvement with respect to all metrics. The increase
w.r.t. CTR and CVR are 0.230% and 1.653%, respectively, which is a notable
improvement.

  * •

The increase w.r.t. CTCVR is 1.886%. CTCVR indicates how likely users are to
purchase the recommended items. Such improvement can result in a substantial
rise in total consumption on the platform.

  * •

The rise w.r.t. GTV-CC is 3.627%. The remarkable uplift demonstrates the
outstanding capability of our system in predicting users’ living needs that
has no historical record.

We further calculate the Kullback-Leibler divergence (Kullback and Leibler,
1951) (KLD) between real user order distribution by category and the average
proportional allocation given by the online engine with/without our system.
Lower KLD indicates the quotas match better with real user order distribution,
which can be regarded as real user needs distribution. The results of
different time periods throughout the day are shown in Table 6. The time
periods are separated based on the business characteristics of the platform
during each hour. Similar hours are grouped within a single period.

Table 6. KLD of different time periods between real user order distribution and quotas given with/without NEON. Time Period | Kullback-Leibler Divergence | Improvement
---|---|---
w/o NEON | with NEON
0-4 | 0.2578 | 0.2201 | -14.62%
5-8 | 0.2237 | 0.1907 | -14.75%
9-10 | 0.2179 | 0.1902 | -12.71%
11-12 | 0.2408 | 0.2138 | -11.21%
13-16 | 0.2323 | 0.2053 | -11.62%
17-19 | 0.2353 | 0.2096 | -10.92%
20-24 | 0.2333 | 0.1995 | -14.49%

From the results, it can be seen that in all time periods, the Kullback-
Leibler Divergence between the actual user order distribution by category and
the average proportional allocation generated by the online engine with NEON
is significantly less than that without our NEON. The percentage of decrease
(improvement) is 12.90% on average. This indicates that the quotas produced by
the online engine with our system are more aligned with the actual user
consumption distribution, or the real-life user living needs, compared to the
ones generated without our system. This can be regarded as evidence of our
model’s effectiveness in addressing the intricate impact of spatiotemporal
context, and further confirms our system’s strong ability for predicting fine-
grained user needs.

###  5.2. Guess-you-like Recommendation

This section assesses NEON’s performance in guess-you-like recommendation,
highlighting its ability on needs-meeting way prediction.

####  5.2.1. Deployment Scenario

Meituan designs a Guess-you-like page, which user may be guided to during
their leisure time. Typically, users browse this page to satisfy their
unnecessary living needs, such as entertainment or beauty, etc. In this page,
they purchase items they need and also like. To provide more choices for users
in the categories of life services they need, the recommendation list should
have more items in those categories. In this page, we assume that users are
more concerned with being recommended items they like. Therefore, we do not
maintain the proportion of a category in the "Guess-you-like" recommendation
list just because there is a slight possibility that it is essential, as users
can use modules other than Guess-you-like such as the search button to find
necessary life services. To ensure enough items are in the needed category,
the online engine generates new quotas for Guess-you-like page based on the
quotas for homepage recommendation list and the in store/via delivery score
output by our system.

In summary, the needs-meeting way prediction results of NEON are used to
further adjust the quotas of different life services in Guess-you-like
recommendation list.

####  5.2.2. Experiment Setting

We conduct online A/B tests involving about 7 million users over a period of
two weeks. We randomly divide users into two buckets, each of which has a
similar amount of users, and assign them different methods of generating the
quotas of categories in the Guess-you-like page. Specifically, for the first
bucket we adopt the same strategy as in the homepage recommendation. For the
second bucket, we calculated the score of both needs-meeting ways, and turn
higher the quotas of categories whose needs-meeting way gets a higher score.
As for metrics, we use CTR, CVR, CTCVR, Negative Feedback Rate for Unique
Visitor (NFR-UV), Negative Feedback Rate for Page View (NFR-PV) to measure the
quality of the recommendation list in the Guess-you-like page. NFR-UV and NFR-
PV emphasize that the recommendation list should not contain items users
dislike.

####  5.2.3. Performance

Under the aforementioned settings, we conduct extensive online A/B
experiments. The results are shown in Table 7. We list our observations as
follows:

Table 7. Overall results of A/B tests on Guess-you-like page recommendation. Metric | w/o NEON | with NEON | Improvement
---|---|---|---
CTR | 9.5707$\times 10^{-2}$ | 9.5647$\times 10^{-2}$ | -0.063%
CVR | 1.3568$\times 10^{-1}$ | 1.3606$\times 10^{-1}$ | +0.280%
CTCVR | 1.3014$\times 10^{-2}$ | 1.2985$\times 10^{-2}$ | +0.218%
NFR-UV | 1.0128$\times 10^{-4}$ | 1.0294$\times 10^{-4}$ | +1.605%
NFR-PV | 8.3283$\times 10^{-5}$ | 8.6726$\times 10^{-5}$ | +3.342%

  * •

The performance gain w.r.t. CVR, CTCVR, and OV are 0.280%, 0.218%, and 0.310%,
respectively. Such improvement resulting from adjusting the quotas with the
assistance of the needs-meeting way prediction module can lead to a
significant increase in the consumption amount.

  * •

The NFR-UV and NFR-PV decrease by 1.57% and 3.31% respectively, indicating
that the recommendation engine is able to recommend fewer items that users
dislike in the Guess-you-like page by adjusting the quotas with the aid of the
needs-meeting way prediction module in our system.

We further calculate the performance increase on some popular categories of
life services in Guess-you-like page. The results are shown in Table 8. From
the result, we can observe that:

  * •

For all these categories there is average relative improvement of 3.801%
w.r.t. CVR and 3.765% w.r.t CTCVR. With such improvements, all these
categories can enjoy a notable increase in order volume on Meituan platform.

  * •

Among all these categories, the rise on Kids category is the most significant,
which is 8.723% w.r.t. CVR and 9.691% w.r.t. CTCVR.

  * •

There are also slight decreases w.r.t. CTR of 0.034% for Food Delivery,
-0.449% for Beauty, and -0.595% for Hotel. These decreases are within the
typical fluctuations observed in the market.

The above results, which demonstrate the successful implementation of our
system in guess-you-like recommendation, further illustrate the efficacy of
our system, particularly in needs-meeting way prediction.

Table 8. The A/B tests results on several popular categories of life services in Guess-you-like page. Category | Metric | w/o NEON | with NEON | Improvement
---|---|---|---|---
Food | CTR | 1.8298$\times 10^{-2}$ | 1.8292$\times 10^{-2}$ | -0.034%
CVR | 2.8841$\times 10^{-1}$ | 2.8987$\times 10^{-1}$ | +0.506%
CTCVR | 5.2774$\times 10^{-3}$ | 5.3023$\times 10^{-3}$ | +0.472%
Beauty | CTR | 1.8769$\times 10^{-2}$ | 1.8684$\times 10^{-1}$ | -0.449%
CVR | 1.2583$\times 10^{-3}$ | 1.2889$\times 10^{-3}$ | +2.427%
CTCVR | 2.3616$\times 10^{-4}$ | 2.4081$\times 10^{-4}$ | +1.967%
Kids | CTR | 1.7717$\times 10^{-2}$ | 1.7874$\times 10^{-2}$ | +0.890%
CVR | 7.8825$\times 10^{-3}$ | 8.5701$\times 10^{-3}$ | +8.723%
CTCVR | 1.3965$\times 10^{-1}$ | 1.5319$\times 10^{-4}$ | +9.691%
Hotel | CTR | 1.6567$\times 10^{-2}$ | 1.6469$\times 10^{-2}$ | -0.595%
CVR | 4.1454$\times 10^{-2}$ | 4.2923$\times 10^{-2}$ | +3.546%
CTCVR | 6.8678$\times 10^{-1}$ | 7.0690$\times 10^{-4}$ | +2.930%

###  5.3. Message Pop-up Recommendation

In this section, we test our model’s performance in message pop-up
recommendation to observe its potential need prediction ability.

####  5.3.1. Deployment Scenario

In the case where a user has a living need that has never or rarely been
fulfilled on Meituan platform, he/she probably won’t launch Meituan mobile
app, as they may not be aware that this need can be satisfied on Meituan or
may not be accustomed to fulfilling this need on Meituan. So, if our engine
runs in the background of the phone and detects the user’s potential living
need, it will send a message pop-up to the user with a recommendation for a
life service solution, if the user allows it. The message pop-up recommends
the user a life service that is in the category of the highest score output by
our system.

In brief, with the ability of potential needs prediction, NEON is deployed for
message pop-up recommendation.

####  5.3.2. Experiment Setting

We conduct online A/B tests involving one million users over a period of two
weeks. The users are randomly divided into two buckets of similar volume and
are assigned different strategies for selecting items to be sent in message
pop-ups. For the first bucket, the message pop-up recommends items that belong
to the category with the highest score output by our system. For the second
bucket, in each hour of the day, we calculate the popularity and average CTR
of each category. We distribute the traffic for message pop-ups to the
categories with the most popularity and highest average CTR.

We use CTR, CVR, CTCVR, OV, Number of Cold-start Customers (NCC) to measure
the performance of our system in potential need prediction. NCC represents the
number of customers who purchase a lifestyle service that they have never
acquired on the platform previously.

####  5.3.3. Performance

The results of the online A/B tests are shown in Table 9. We can observe that:

  * •

By replacing the algorithm based on popularity and average CTR with our living
need prediction system NEON to determine the category of recommendation in
message pop-up, all metrics show significant improvement. CTR, CVR, CTCVR, and
OV increase by 8.21%, 78.64%, 85.71%, and 95.92%, respectively.

  * •

NCC increases by 74.26%, indicating that the online deployment of our system
in message pop-up recommendations results in a 74.26% increase in the number
of customers purchasing a life service that they have not previously acquired
on the Meituan platform, via the message pop-up feature.

Our system is able to accurately detect the potential needs of users for a
specific lifestyle service, even in instances where they have never previously
purchased that service on the Meituan platform, and accordingly deliver
targeted message pop-ups to them. This result strongly demonstrates the
exceptional capability of our system in predicting users’ potential needs.

Table 9. Results of A/B tests on message pop-up recommendation. Metric | CTR | CVR | CTCVR | OV | NCC
---|---|---|---|---|---
Improv. | +8.21% | +78.64% | +85.71% | +95.92% | +74.26%

In summary, the success achieved by our NEON system on three downstream
recommendation tasks proves its effectiveness on fine-grained need prediction,
needs-meeting way prediction, and potential need prediction, respectively. The
significant performance gain in real applications can lead to huge benefits.

##  6\. Related Work

As discussed above, in this work we explore the problem of living needs
prediction, defined as predicting the specific living needs of a user given
the spatiotemporal context. Thus, there are two closely related research
topics: demand forecasting and spatiotemporal activity prediction.

###  6.1. Demand Forecasting

Demand forecasting aims at predicting the quantity of a product or service
that consumers will purchase. It helps in making informed decisions on
inventory management, production scheduling, pricing strategy, etc. The
problem of demand forecasting is broad and multifaceted, affecting many
different industries, including restaurant (Lasek et al., 2016), manufacturing
(Seyedan and Mafakheri, 2020), retail (Ren et al., 2020), tourism (Song et
al., 2019), energy (Hernandez et al., 2014), transportation (Tskeris and
Tsekeris, 2011), etc.

To address the problem of demand forecasting, researchers have proposed
various methods which can be broadly classified into three categories:
statistical models (Lasek et al., 2016; Fattah et al., 2018; Huber et al.,
2017; Priyadarshi et al., 2019; Wang et al., 2019), machine learning models
(Reynolds et al., 2013; Sellers and Shmueli, 2010; Aishwarya et al., 2020; Ma
et al., 2016), and deep learning models (Raza, 2017; Duncan and Elkan, 2015;
Lakshmanan et al., 2020; Kilimci et al., 2019). Statistical models, such as
exponential smoothing (Silva et al., 2019), are well-suited for long-term
demand forecasting as they are based on historical trends and patterns.
However, they are not adept at handling variations or outliers in the data,
making them unsuitable for volatile or short-term demand forecasting. On the
other hand, machine learning models for demand forecasting, such as Random
Forest based models (Ramya and Vedavathi, 2020; Abbasi et al., 2019), are
efficient at short-term demand forecasting, but their performance drops when
it comes to long-term forecasting. Deep learning models such as LSTM based
models (Lakshmanan et al., 2020) and GAN based models (Husein et al., 2019)
have the ability to capture complex patterns and dependencies in the data,
making them suitable for both short-term and long-term demand forecasting.
However, they require a large amount of data to work well.

Existing demand forecasting methods can not handle the problem of living needs
prediction. These methods focus on the overall demand for a particular product
or service in a market, but in this work, we aim at predicting the need of a
specific consumer. What’s more, demand forecasting methods predict demands in
several months or years, while in this work we predict a user’s need at a
specific time and location.

###  6.2. Spatiotemporal Activity Prediction

Spatiotemporal activity prediction aims to predict the activity of a user at a
given time and location. Previous works have employed various methods to
perform spatiotemporal activity prediction. One popular approach is to build a
tensor using historical data and then conduct tensor factorization to learn
intrinsic association (Fan et al., 2019; Zheng et al., 2010; Bhargava et al.,
2015). For example, Fan et al. (Fan et al., 2019) propose to integrate tensor
factorization with transfer learning for online activity prediction.
Additionally, WDGTC (Li et al., 2020) proposes a low-rank tensor decomposition
and completion framework for passenger flow prediction by introducing L1-norm
and Graph Laplacian penalties. Recently, researchers have introduced Graph
Convolutional Networks (Kipf and Welling, 2016) (GCNs) to achieve high
performance in spatiotemporal activity prediction. For example, SA-GCN (Yu et
al., 2020) develops a Graph Convolutional Network with meta path-based
objective function for App-usage prediction task. Furthermore, DisenHCN (Li et
al., 2022) utilizes a heterogeneous hypergraph to model fine-grained user
similarities, resulting in significant performance gains.

However, existing works on spatiotemporal activity prediction focus on
predicting the specific activities of people, while our work focuses on the
general living needs which are the driving force behind specific consumption
behaviors. What’s more, these works mainly focus on either online or offline
activities, but in our work, we predict living needs can be satisfied both in
store (offline) and via delivery (online) by different kinds of life services,
which is beyond the capabilities of existing methods.

##  7\. Conclusion and Future Work

In this work, we approach the new problem of living needs prediction, which is
critical in life services platforms. We present the NEON system in Meituan,
consisting of three phases, feature mining, feature fusion, and multitask
prediction. Large-scale online A/B testing in three downstream applications,
along with extensive offline evaluation, strongly confirm the effectiveness of
our system. As for future work, we plan to test NEON’s performance in more
downstream applications.

## Acknowledgement

This work is supported in part by National Key Research and Development
Program of China under 2022YFB3104702. This work is supported in part by
National Natural Science Foundation of China under 62272262, 61971267, and
61972223. This work is supported in part by a grant from the Guoqiang
Institute, Tsinghua University under 2021GQG1005. This work is supported in
part by Beijing National Research Center for Information Science and
Technology. This work is also supported by Meituan.

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##  Appendix A APPENDIX FOR REPRODUCIBILITY

###  A.1. Model Implementation Details

In this section we provide implementation details of our system. Due to the
massive amount of training data and the high demand for low latency in our
online system, we simplify each network within the framework. In our final
implementation, feature merging network $h^{M}$ is a one-layer fully-connected
network of 120 output units (120D FC). User preference network $h^{U}$ is a
340D FC. Shared network $E^{S}$ and expert networks $E^{W}$/$E^{N}$ are 256D
FCs. Need prediction network $t^{N}$ is a 10D FC. In-store/delivery
classification network $t^{W}$ is a two-layer perceptron, which has 10 hidden
units and 2 output units. Complexifying these neural networks can help us
further optimize performance, but at the same time, it would lead to an
increase in system latency. The activation function of all mentioned networks
is ReLU (Glorot et al., 2011). We adopt batch normalization (Ioffe and
Szegedy, 2015) right after $h^{M}$, $h^{U}$, $E^{S}$, $E^{P}$, and $E^{N}$.
Following such implementation, we conduct rich online and offline experiments
to prove the effectiveness of our model, which are shown in Section 4 and
Section 5.

###  A.2. Dataset

We conduct our offline experiment on a real-world dataset at the scale of
billions. The dataset is a sampling of all purchase records in 2022 on the
platform. We sample the records according to the percentage of purchases of
each kind of life service. The dataset includes more than 7 billion real
purchase records from 65 million users. Each instance in the dataset includes
user profile, time, location, and other real-time environmental factors, and
the kind of life service the user purchase. The type of life service consumed
by the user reflects their actual living need in the spatiotemporal context.
Following existing works (Cheng et al., 2012; Palumbo et al., 2018), we
randomly sample 80% of the dataset as the training set, and 20% as the test
set.

###  A.3. Metrics

We design a metric named Sort Accuracy (SA) to measure the performance of
systems on our problem. The metric SA can be defined as follows. For user
scene $i$, our system outputs scores for all kinds of living needs. We sort
the living needs by their scores and get a list. We define Relative Ranking
Error as the difference between the actual ranking position and the ideal
ranking position (the first position) of the ground truth need, and further
define Maximum Ranking Error as the maximum relative ranking error any system
can give for a user scene (the number of categories of user needs - 1). For
example, in our system which handles 10 types of living need, for user scene
$i$, if the ground truth need is ranked third, then the relative ranking error
is 2 (2 = 3-1), and the maximum ranking error is 9 (9 = 10-1). Then Sort
Accuracy (SA) can be defined as follows,

(14) $\text{SA}=\text{average}_{i\in T}\left(1-\frac{\text{Relative Ranking
Error}}{\text{Maximum Ranking Error}}\right)$

where $T$ denotes the testing set on which the metric is calculated. We first
calculate $1-\text{Relative Ranking Error}/\text{Maximum Ranking Error}$ of
every user scene in the testing set and then compute the average. We then
define Via-delivery Sort Accuracy (VDSA) and In-store Sort Accuracy (ISSA) to
measure systems’ performance on delivery and in-store living needs.

(15) $\text{VDSA}=\text{average}_{i\in T_{VD}}\left(1-\frac{\text{Relative
Ranking Error}}{\text{Maximum Ranking Error}}\right)$ (16)
$\text{ISSA}=\text{average}_{i\in T_{IS}}\left(1-\frac{\text{Relative Ranking
Error}}{\text{Maximum Ranking Error}}\right)$

$T_{VD}$ and $T_{IS}$ denote sets of testing samples where the ground truth
needs-meeting way is via delivery and in store, respectively. In the
following, we will use these metrics to measure the living needs prediction
performance of our system and baseline systems.

###  A.4. Baselines

To illustrate the effectiveness of our system, we compare it with two
baselines widely in actual production environments, including DIN (Zhou et
al., 2018), DNN (Cheng et al., 2016), DCN (Wang et al., 2017), ESMM (Ma et
al., 2018a), and MMOE (Ma et al., 2018b). We will provide a detailed
description of these baselines in the appendix. DIN is a recommendation
algorithm that leverages deep neural networks to analyze users’ historical
behavior and make predictions about their potential interests. It uses an
attention-based mechanism to weigh the importance of different historical
behaviors for predicting the current interest of a user. As for DNN, We follow
the design of Wide & Deep learning to build a Deep Neural Network (DNN) based
system for our task. It can learn both simple and complex relationships in the
data. DCN is proposed to keep the benefits of a DNN model while introducing a
cross network that is more efficient in learning certain bounded-degree
feature interactions. It applies feature crossing at each layer, and it
doesn’t require manual feature engineering, adding minimal extra complexity to
the DNN model. ESMM estimates post-click conversion rates for recommendation
systems by using sequential user actions and a feature representation transfer
learning strategy to alleviate sample selection bias and data sparsity. MMoE
is a multi-task learning approach that learns to model task relationships from
data. It adapts the Mixture-of-Experts (MoE) structure to multi-task learning
by sharing the expert submodels across all tasks, while also having a gating
network trained to optimize each task. For a clear comparison, the input
features for all baselines are kept the same as those for our model.

###  A.5. Additional Ablation Study

To gain a deeper understanding of the impact of each component design of our
model, we also conduct ablation studies with a particular focus on the effects
of the group behavior pattern features and the spatiotemporal context
features. When removing the group behavior pattern features from the model
input, the performance decreased by 1.06%. When removing the spatiotemporal
context features from the model input, the performance decreased by 1.46%.
These results provide compelling evidence of the pivotal role that these two
features play in accurately predicting users’ daily living needs.