# Quantifying and Mitigating Popularity Bias in
Conversational Recommender Systems

Allen Lin  Texas A&M UniversityCollege Station, TexasUSA<EMAIL_ADDRESS>,
Jianling Wang  Texas A&M UniversityCollege Station, TexasUSA<EMAIL_ADDRESS>,
Ziwei Zhu  George Mason UniversityFairfax, VirginiaUSA<EMAIL_ADDRESS>and
James Caverlee  Texas A&M UniversityCollege Station, TexasUSA
<EMAIL_ADDRESS>
(2022)

###### Abstract.

Conversational recommender systems (CRS) have shown great success in
accurately capturing a user’s current and detailed preference through the
multi-round interaction cycle while effectively guiding users to a more
personalized recommendation. Perhaps surprisingly, conversational recommender
systems can be plagued by popularity bias, much like traditional recommender
systems. In this paper, we systematically study the problem of popularity bias
in CRSs. We demonstrate the existence of popularity bias in existing state-of-
the-art CRSs from an exposure rate, a success rate, and a conversational
utility perspective, and propose a suite of popularity bias metrics designed
specifically for the CRS setting. We then introduce a debiasing framework with
three unique features: (i) Popularity-Aware Focused Learning to reduce the
popularity-distorting impact on preference prediction; (ii) Cold-Start Item
Embedding Reconstruction via Attribute Mapping, to improve the modeling of
cold-start items; and (iii) Dual-Policy Learning, to better guide the CRS when
dealing with either popular or unpopular items. Through extensive experiments
on two frequently used CRS datasets, we find the proposed model-agnostic
debiasing framework not only mitigates the popularity bias in state-of-the-art
CRSs but also improves the overall recommendation performance.

Conversational Recommender System, Popularity Bias, Debiasing

††journalyear: 2022††copyright: acmcopyright††conference: Proceedings of the
31st ACM International Conference on Information and Knowledge Management;
October 17–21, 2022; Atlanta, GA, USA.††booktitle: Proceedings of the 31st ACM
International Conference on Information and Knowledge Management (CIKM ’22),
October 17–21, 2022, Atlanta, GA, USA††price: 15.00††isbn:
978-1-4503-9236-5/22/10††doi: 10.1145/3511808.3557423††ccs: Information
systems Recommender systems

##  1\. Introduction

Recommender systems have become an indispensable tool in everyone’s daily
life. Ranging from E-commerce, to multimedia services, and to E-education
platforms, recommender systems alleviate information overload by connecting
users to their items of interest. While they have been one of the success
stories of AI in practice, one long-lasting challenge faced by recommender
systems is popularity bias, which refers to the phenomenon of the minority
popular items being overly exposed to users while the majority unpopular items
do not receive their deserved attention (Zhu et al., 2021b; Abdollahpouri et
al., 2021; Fu et al., 2021a; Abdollahpouri, 2019; Kamishima et al., 2014;
Abdollahpouri et al., 2019b; Wei et al., 2021; Boratto et al., 2021;
Brynjolfsson et al., 2006). This popularity bias greatly limits the
opportunities for users to discover these less exposed items (Fu et al.,
2021a; Wei et al., 2021; Steck, 2011) and the system’s potential to learn an
unbiased view of the user’s true preferences (Zhu et al., 2021b; Park and
Tuzhilin, 2008). To solve such a critical issue, extensive efforts have been
made to investigate and mitigate popularity bias in recommender systems (Zhu
et al., 2021b; Abdollahpouri and Burke, 2019; Kamishima et al., 2014; Steck,
2011).

Figure 1. Evidence of different perspectives of popularity bias in CRSs

Evidence of Popularity bias in CRSs.

Recently, a new form of interactive recommendation system – the conversational
recommender system (CRS) – has shown great success in enhancing
personalization through multi-turn interactions (i.e., dialogue). In this way,
the system can elicit users’ current and detailed preferences, which can lead
to justifiable and highly personalized recommendations. Similar to traditional
recommender systems, a CRS also takes into consideration the past user-item
interactions when making recommendations. Therefore, it would be reasonable to
assume that a CRS is also affected by popularity bias similar to a traditional
recommender. However, despite the promising functionality and wide range of
potential applications of CRSs, how to quantify and mitigate the issue of
popularity bias in CRSs remains understudied.

Hence, this paper first illustrates the existence of popularity bias in a
multi-round CRS from three perspectives: (i) Exposure Rate, where we find
popular items are exposed to users at a significantly higher rate than others,
even when these other items match all preferences specified by the user; (ii)
Success Rate, where we find items with high popularity have much higher
chances of being successfully recommended; and (iii) Conversational Utility,
where we find popular items are recommended in earlier rounds than other
items, violating the assumption of an unbiased recommender. Based on these
observations, we propose a suite of popularity bias metrics designed
specifically for the CRS setting.

We then diagnose the root causes of the observed popularity bias. First, we
find that item popularity can significantly impact the magnitude of the item
embedding in the recommender component which drives the predicted preference
score. Since popular items occur more in the training set (through user-item
interactions), the embeddings of popular items are updated more frequently,
leading to greater magnitudes, and hence higher predicted scores. Second, we
find that cold-start items are typically ill-modeled in existing CRS, meaning
that they are rarely successfully recommended to users. Finally, we find that
the policy agent which guides the actions of a CRS at each step may further
amplify popularity bias by prioritizing specific attributes associated with
popular items. Based on this diagnosis, we propose a three-stage debiasing
framework that generalizes across specific CRS implementations. To combat the
item magnitude problem, we introduce Popularity-Aware Focused Learning (PAL)
which is designed to help the recommender component learn a more fine-grained
set of embeddings for the unpopular items without sacrificing the
effectiveness of the learned embeddings for popular items. To better model
cold-start items, we propose a Cold-Start Item Embedding Reconstruction via
Attribute Mapping (CSM) to avoid the problem of randomly initialized
embeddings for cold-start items by transferring representations from warm-
start items to the cold-start ones. To improve the policy agent, we design
Dual-Policy Learning (DPL) to train one policy network on popular items and
one policy network on unpopular items. Through extensive experiments on both
Yelp and Lastfm, we demonstrate that the proposed three-stage debiasing
framework can significantly mitigate popularity bias across existing baseline
CRS methods by an average of 42.5% while improving their overall
recommendation success rate by 12.3%, all while keeping the average
conversational turns the same.

##  2\. Preliminaries

The goal of a CRS is to improve the preference elicitation process by directly
asking the user’s current preference on a set of domain specific attributes
that characterize every item in the itemset. In this work, we follow the
System Ask - User Responds (SAUR) setting (Zhang et al., 2018) to investigate
popularity bias in multi-round CRS. Formally let $U$ denote the user set, $V$
denote the itemset, and $A=a_{1},a_{2},...,a_{m}$ denote a set of m domain-
specific attributes used to systematically characterize all items in $V$.

At each turn, a CRS calls upon its conversation component, which is typically
a policy agent, to decide whether to ask the user’s preference on a specific
attribute or make a recommendation. If the policy agent decides not enough
preference evidence has been collected, it will pick one attribute $a$ from
the set of unasked attributes to prompt the user. When prompted with the
question, the user is assumed to provide her preference $p$ on the asked
attribute $a$. Upon receiving the user’s preference, the policy agent updates
its current belief state $s$ by adding the (attribute, preference) pair to it.
If the policy agent decides enough information has been collected after t
turns of interaction, the CRS then calls upon its recommender component to
make a list of recommendations for the user. Unlike a traditional static
recommender which ranks all items in the entire itemset, the recommender
component of a CRS only ranks items in the candidate itemset $V_{cand}$ – the
itemset that contains only items with attributes perfectly matching all
(attribute, preference) pairs in the current belief state
$s_{t}=[(a_{1},p_{1}),...,(a_{t},p_{t})]$. After ranking all items in
$V_{cand}$, the CRS recommends the top K items to display to the user. If the
user accepts the recommendation, then the system quits. If the user rejects
all the recommended items, the system repeats the above-introduced cycle by
calling upon its policy agent to decide the next action to take. This process
continues until the user quits due to impatience or a predefined maximum
number of allowed conversational turns has been reached.

##  3\. Evidence of Popularity Bias in CRS

In this section, we conduct data-driven analysis on the Lastfm dataset for
music artist recommendation with three state-of-the-art CRSs: SCPR (Lei et
al., 2020b), EAR (Lei et al., 2020a), and CRM (Sun and Zhang, 2018), to
illustrate the existence of popularity bias in a multi-round CRS from three
perspectives:

Exposure Rate Perspective. A CRS starts by collecting a set of user
preferences, from which the system forms a candidate item set containing all
items matching the collected user preferences so far. Once the system
determines enough information has been collected, it selects the top-N items
from the candidate set to generate the recommendation set. As shown in Figure
1(a), the average popularity of items that get recommended by CRM is almost 8
times higher than the average popularity of items in the candidate set. A
similar pattern is observed in EAR and SCPR, indicating popular items are much
more likely to get recommended (exposed) than unpopular items. However, in a
completely unbiased CRS, since both the candidate set and the recommendation
set contain items perfectly matching all preferences specified by the user
(i.e., equally qualified items), the overall average popularity for both sets
should not deviate excessively. The observed phenomenon suggests the existence
of a strong exposure based bias in all three CRS models.

Success Rate Perspective. Next, we investigate whether there exists a
correlation between an item’s popularity and recommendation success rate
(i.e., how likely the item will be recommended to the user within the allowed
turns of interactions). In a completely unbiased system, an item’s popularity
should be completely irrelevant to its recommendation success rate. Any
correlation, either positive or negative, is an indication of popularity bias
in the CRS. As we can observe from Figure 1(b), all three models – SCPR, EAR,
and CRM – exhibit strong positive correlations between an item’s popularity
and its recommendation success rate. For example, in EAR, items with
popularity greater than 0.01 – the 75th percentile of the sorted popularity of
all items – have over 75% recommendation success rate, while items with
popularity lesser than 0.01 having significantly lower. That is, items with
high popularity have much higher chances of being successfully recommended,
indicating the existence of strong success rate bias in SCPR, EAR, and CRM.

Conversational Utility Perspective. Unlike traditional static recommenders, a
CRS operates under a multi-round interaction setting, in which a user might
leave due to impatience when the session is taking too long. Intuitively,
items requiring fewer turns of user interactions (i.e., dialogue) to get
successfully recommended should be considered more advantageous than those
requiring more. Thus in an unbiased CRS, an item’s required turns of user
interactions should be completely independent of its popularity. However, as
shown in Figure 1(c), all three models exhibit strong inverse correlations
between an item’s popularity and its required turns of user interaction to get
successfully recommended. This observation shows that an item’s conversational
utility is heavily dependent upon its popularity, indicating the existence of
strong conversational utility bias in SCPR, EAR, and CRM.

##  4\. Quantifying Popularity Bias in CRS

After demonstrating the existence of popularity bias in multi-round CRSs, we
formally define three bias metrics for quantifying the degree of popularity
bias from each of the three perspectives introduced in Section 3.

Popularity Correlation with Exposure Rate (PER). Given the deviation of
average item popularity between the recommended items and the candidate items
as illustrated in Figure 1(a), we seek to measure popularity bias from a more
comprehensive perspective that also accounts for the rank of popular items on
the recommendation list (ranking utility). To start, we consider the
correlation between popularity and exposure rate first formally quantified in
(Fu et al., 2021a). Let $A$ be a set of $m$ attributes associated with an
itemset $V$, (Fu et al., 2021a) defines the exposure-based bias at the
$n^{th}$ turn to be:

(1) $E_{A_{n}}[\hat{V}_{u,n}]=\sum_{i=1}^{m}\mathbbm{1}[a_{i}\in
A_{n}]\rho(a_{i}|\hat{V}_{u,n})P(a_{i}|\hat{V}_{u,n}),$

where $\hat{V}_{u,n}$ denotes the set of potential recommendations made by the
CRS at the $n^{th}$ interaction (turn). $A_{n}$ denotes the set of attributes
associated with all invoked questions till the $n^{th}$ turn, and
$\mathbbm{1}[.]$ denotes whether an attribute $a$ exists in $A_{n}$.

Figure 2. Success Rate Bias Measured via Gini Coefficient

The success rate bias is demonstrated by the inequality gap. the larger the
area the more severe the bias.

$\rho(a_{i}|\hat{V}_{u,n})$ measures the ranking utility of all popular items,
defined based on a certain attribute $a_{i}$, on the recommendation list. The
higher the popular items are ranked, the greater $\rho(a_{i}|\hat{V}_{u,n})$
will be. $P(a_{i}|\hat{V}_{u,n})$ measures the number of popular items on the
recommendation list. The more frequently popular items are recommended, the
greater $P(a_{i}|\hat{V}_{u,n})$ will be. Together, $E_{A_{n}}[\hat{V}_{u,n}]$
considers both the ranking utilities of popular items and the frequency of
them being recommended.

However, Equation 1 cannot be easily adapted to the conventional interaction-
frequency based definition of popularity. Referring back to Equation 1, the
notion of popularity is based upon a particular attribute $a_{i}$. For
instance, in a Movie dataset, the attribute production has over 15 values, but
Columbia Pictures, Disney, and Lions Gate Films alone account for more than
50% of the movies in the training data. In this case, items (movies) that are
produced by Columbia Pictures, Disney, or Lions Gate Films would be deemed as
popular items; however, such a definition has a slight limitation. Referring
back to the same Movie dataset, the attribute Duration has 4 different values,
but ¿=90min alone accounts for more than 60% of the movies in the training
data. In this case, items (movies) with durations greater or equal to 90
minutes would be classified as popular items; however, there could be an item
(movie) that has a duration of less than 90 minutes but is produced by
Columbia Pictures. In this case, this movie would be classified as popular by
the attribute production but unpopular by the attribute duration. Therefore,
to adapt to a more generalizable definition of popularity, we modify the
existing metric to be:

(2) $E_{n}[\hat{V}_{u,n}]=\rho(\hat{V}_{u,n})P(\hat{V}_{u,n}),$

with $\rho(a_{i}|\hat{V}_{u,n})$ updated to be:

$\rho(\hat{V}_{u,n})=\sum_{v\in\hat{V}_{u}}\frac{\mathbbm{1}[v\in\hat{V}_{u}^{pop}(t^{pop})]}{log(rank(v)+1)},$

and $P(a_{i}|\hat{V}_{u,n})$ updated to be:

$P(\hat{V}_{u,n})=\frac{|\hat{V}_{u,n}^{pop}(t^{pop})|}{|\hat{V}_{u}|},$

where $t^{pop}$ denotes the popularity threshold and
$\hat{V}_{u,t}^{pop}(t^{pop})$ denotes recommended items that have popularity
greater or equal to the threshold. We adopt the conventional definition of
$\frac{|U_{interacted}|}{|U|}$ for measuring the popularity of an item and
categorize items with popularity greater than the 80th percentile as popular
(head) items (Abdollahpouri et al., 2019b, a, 2017). Similar to Equation 1,
this metric (Equation 2) also measures how frequently popular items are
recommended (exposed) to the user along with their ranking utilities; however,
this metric is attribute independent, making it more generalizable and
consistent across different datasets. Formally we define the popularity
correlation with exposure rate (PER) to be:

(3) $PER=\frac{1}{|I|}\sum_{(u,i)\in
I}\frac{1}{N}\sum_{n=1}^{N}E_{n}[\hat{V}_{u,n}],$

where $I$ denotes the user-item interactions and $N$ denotes the number of
total interactions (turns).

Popularity Correlation with Success Rate (PSR). While Figure 1(b) provides the
illustration, to the best of our knowledge, there has not been a well-defined
metric for quantifying the degree of popularity bias in a multi-round CRS from
a recommendation success rate perspective. However, a few previous works have
demonstrated the effectiveness of using the Gini Coefficient for measuring the
correlation between recommendation accuracy and item popularity (Zhu et al.,
2021a; Brown, 1994; Shani and Gunawardana, 2011). Inspired by these works, we
formally define the popularity correlation with success rate (PSR) to be:

(4) $PSR=1-2\int_{0}^{1}SR(V_{sort})dV_{sort},$

where $V_{sort}$ denotes a set of items sorted in ascending popularity, and
$SR(V_{sort})$ denotes the Lorenz curve constructed with the x-axis
representing items sorted in ascending popularity and the y-axis representing
the average success rate accumulated so far. As illustrated in Figure 2, in a
perfectly unbiased CRS, the average success rate should be accumulated at the
same rate for all items regardless of their popularity, making $SR(V_{sort})$
a straight line with a constant slope of 1. In this case, the AUC would be
0.5, resulting in a Gini Coefficient of 0. However, when a positive
correlation exists between popularity and success rate, $SR(V_{sort})$ would
instead be a concave hyperbolic curve with increasing slope, making the AUC
less than 0.5. In this case, the Gini Coefficient would be a positive number
ranging from (0,1), the closer to 1 the stronger the bias.

Popularity Correlation with Conversational Utility (PCU). To the best of our
knowledge, this is the first work that investigates popularity bias in a
multi-round CRS from a conversational utility perspective. Therefore, there
does not exist any well-defined metric that can be applied. Building upon the
notion that an item’s required turns of user interactions should be completely
independent of its popularity, we calculate the averaged turns of user
interactions for both the popular (head) items and the unpopular (tail) items
and use the difference (gap) between the two as our metric. Formally we define
the popularity correlation with conversational utility (PCU) to be:

(5) $PCU=\frac{1}{(T)}[\frac{1}{|V^{unpop}|}\sum_{v\in
V^{unpop}}U_{turn}(v_{i})-\frac{1}{|V^{pop}|}\sum_{v\in
V^{pop}}U_{turn}(v_{i})],$

where $V^{pop}$ and $V^{unpop}$ respectively denote the popular (head) and
unpopular (tail) itemset, $U_{turn}(v)$ denotes the needed turns of user
interactions to get successfully recommended item $v$. $T$ denotes the total
turns of allowed interactions which serves as the normalizing constant.
Following the convention (Abdollahpouri et al., 2019b, a, 2017), we define
items with popularity greater than the 80th percentile to be popular and items
with popularity less than the 20th percentile to be unpopular.

##  5\. Debiasing Framework

In this section, we identify the three primary causes of popularity bias in
CRSs and propose a three-stage debiasing framework that can be easily
generalized. The framework starts with the popularity-aware focused learning
stage where the recommender component learns to fairly model all items by
taking into account their popularity. After learning an effective set of
embeddings for all items, the framework builds a attribute to item embedding
mapping to reconstruct embeddings for items with zero user interactions (the
cold-start items). Lastly, to help reduce the number of popularity-based false
assumptions that the policy agent makes, the framework separately trains two
policy agents, one for the popular items and one for the unpopular items.

###  5.1. Popularity-Aware Focused Learning (PAL)

Cause. A CRS relies upon its recommender component to rank all the candidate
items when making recommendations to a user. This component is typically first
trained separately as a static recommendation system then fine-tuned via
reinforcement learning in an offline simulation (Sun and Zhang, 2018; Lei et
al., 2020a; Xu et al., 2021a). Similar to a typical collaborative
recommendation system, the recommender component learns a set of user, item,
and attribute embeddings that will be used to predict a user’s interest in a
particular item. As shown in (Lei et al., 2020a), one way to calculate the
predicted rating is:

(6) $\hat{y}(u,v,A_{u})=\textbf{u}^{\intercal}\textbf{v}+\sum_{a_{i}\in
A_{u}}\textbf{v}^{\intercal}\textbf{a}_{i}$

where u and v denote the embedding for user u and item v, and $\textbf{a}_{i}$
denotes the embedding for a specific attribute in the user’s preferred
attribute set $A_{u}$. From the above equation, we can see that the item
embedding v plays a vital role in the rating prediction, as it is used both to
predict the general interest of the user on the target item and the affinity
between the target item and the user’s preferred attributes. However, most
existing CRS neglect the potential issue that popularity bias could bring to
item embeddings. As shown in Figure 3, both EAR and CRM show a strong positive
correlation between the magnitude of an item’s learned embedding – calculated
as the squared sum of all features – and its popularity. Especially for EAR,
the magnitude of the learned embeddings for popular items is significantly
greater than the learned embeddings of the unpopular items. Since an item’s
popularity is calculated by the frequency that such an item appears in the
training set (via past user-item interaction records). Higher popularity is
equivalent to more occurrences (or samples) in the training set. Consequently,
during the training process of the recommender component, the embeddings for
popular items are more frequently updated (e.g., through back-propagation or
other learning schemes), resulting in a greater magnitude. Such a phenomenon
is problematic since this component ranks all candidate items based upon their
predicted ratings. As a result, popular items will have higher predicted
ratings due to the greater magnitude of their embeddings, making them easier
to get recommended by the system.

Figure 3. item popularity vs item embedding magnitude

items with higher popularity have greater embedding magnitudes.

Treatment. To address this issue, we propose a popularity-aware focused
learning formulation for the recommender component. This formulation could be
easily generalized to any objective function used to optimize the recommender
used in a multi-round CRS. For clarity, we show the formulation of our
popularity-aware focused learning on the pairwise Bayesian Personalized
Ranking (BPR) (Rendle et al., 2012) objective function due to its wide usage
in recommendation tasks. Formally the loss function of BPR is defined as:

(7)
$L_{bpr}=\sum_{(u,v,v^{\prime})}-ln\sigma(\hat{y}(u,v,A_{u}))-\sigma(\hat{y}(u,v^{\prime},A_{u}))+\lambda_{\theta}||\theta||^{2},$

where $v$ and $v^{\prime}$ respectively denote an item that has been
interacted by the user and an item that has not. $\sigma$ is the sigmoid
function and $\lambda_{\theta}$ is the regularization parameter. $\hat{y}(.)$
could be any function that calculates the predicted rating based on the user
embedding u, item embedding v, and all attribute embeddings in the user
preferred attribute set $A_{u}$.

Similar to any loss function, the standard BPR finds a global optimal solution
which, as shown in (Beutel et al., 2017), is typically not locally optimal. In
the case of CRS, since the popular items dominate the training set, the global
optimal solution that BPR finds will be heavily tailored towards them,
ignoring the less relevant unpopular items. Consequently, popular items will
have a much higher recommendation success rate compared to the unpopular
items, introducing severe popularity bias into the system. The goal of the
proposed popularity aware focused learning is to help the recommender
component learn a more fine-grained set of embeddings for the unpopular items
without sacrificing the effectiveness of the learned embeddings for popular
items. Formally, our formulation could be generalized to BPR as the following:

(8) $\displaystyle
L_{bpr}=\frac{1}{e^{n_{1}p_{i}}}[-ln\sigma(\hat{y}(u,v,A_{u}))-\sigma(\hat{y}(u,v^{\prime},A_{u}))]+e^{n_{2}p_{i}}||\textbf{v}||^{2}$
$\displaystyle+\lambda_{\hat{\theta}}(||\hat{\theta}||^{2})$

where $p_{i}$ is the popularity of the item $v$. $\frac{1}{e^{n_{1}p_{i}}}$
controls how much the sample is weighted in the loss function, and
$e^{n_{2}p_{i}}$ controls the scale of the regularization on the learned item
embedding. $n_{1}$ and $n_{2}$ are hyperparameters that control the impact of
popularity on an item’s weight and regularization scale. Greater $n_{1}$
denotes lower weights on the popular items and greater weights on unpopular
items; while $n_{2}$ denotes a more significant regularization penalty on the
popular items and a more minor penalty on unpopular items. Note that it is
sufficient to only apply the popularity-correlated regularization parameter
$e^{n_{2}p_{i}}$ to the norm of the item embedding. The recommender component
is only responsible for ranking items within a candidate itemset, so the
magnitude of the user and attribute embeddings becomes irrelevant since the
same user and attribute embeddings are used to calculate the predicted rating.
Applying the popularity-correlated regularization parameter also to the norm
of the user and the attribute embeddings would bring no additional benefits
and might cause the loss function to fail to converge. Adjusting the
importance and scale of regularization of an item based on its popularity
helps the recommender component to focus also on the unpopular items which
would have been poorly-modeled otherwise.

###  5.2. Cold-Start Item Embedding Reconstruction via Attribute Mapping (CSM)

Cause. Cold-start items refer to items with no past user interactions, which
are sometimes excluded from the start in the traditional static recommendation
system. However, one of the intentions for a CRS is to directly elicit
preferences from users so that even cold-start items could be recommended
accurately. Thus it is reasonable to keep cold-start items in the testing set
for a more holistic evaluation. Referring back to Equation 6, we know the item
embedding $v$ plays a significant role in rating prediction; however, cold-
start items were never included in the training of the recommender component.
Their embeddings are often randomly initialized or simply zeros. Therefore,
the predicted rating for a cold-start item is rarely an accurate reflection of
the user’s true interests in the item. Consequently, these cold-start items
are rarely successfully recommended or exposed to users, defeating the purpose
of a CRS.

Treatment. While popularity-aware focused learning alleviates the popularity
bias introduced by the recommender component, the embeddings of cold-start
items remain randomly initialized or simply zeros since they are never
included in the training of the recommender. However, utilizing a unique
property of a CRS, we propose a simple mapping mechanism to reconstruct the
embeddings for cold-start items. In the setting of a CRS, every item,
regardless of its popularity, is associated with a set of predefined
attributes or features that characterizes the item. Based on this property,
for the warm start items, we train a simple feed-forward neural network that
maps items from their one-hotted attribute embeddings to their item embeddings
learned in the previous stage. Using this trained mapping, we reconstruct the
embeddings for cold-start items by feeding their one-hotted attribute
embeddings to the trained mapping neural network. Formally, Let $V^{+}$ denote
the set of warm-start items. Let $emb^{item}_{i^{+}}$ and $emb^{item}_{i^{-}}$
denote the item embedding for a warm-start and a cold-start item respectively.
Let $emb^{onehot}_{i^{+}}$ and $emb^{onehot}_{i^{-}}$ denote the one-hotted
attribute embedding for a warm-start and a cold-start item respectively. We
train a mapping function $f_{\theta}$ with the following loss function:

(9) $argmin_{\theta}\sum_{i\in
V^{+}}(f_{\theta}(emb^{onehot}_{i})-emb^{item}_{i})^{2}+\lambda||\theta||^{2}$

where $\lambda$ is the regularization parameter to prevent overfitting. The
item embedding for the cold-start item can then be constructed as:

(10) $emb^{item}_{i^{-}}=f_{\theta}(emb^{onehot}_{i^{-}})$

In this way, instead of having randomly initialized embeddings, cold-start
items now have embeddings that effectively encode their unique properties,
making their predicted ratings a more realistic reflection of a user’s true
interest.

###  5.3. Dual-Policy Learning (DPL)

Cause. A CRS relies upon a policy agent to decide which action to take at each
interaction with the user. The action space typically includes whether to
elicit the user’s preference on any of the unasked attributes or to make a
recommendation if the agent feels enough information has been collected.
However, without careful modification, the policy agent brings a new form of
popularity bias into the system. In CRS, policy agents are typically first
pretrained with a max entropy attribute selecting strategy, with the goal of
minimizing the number of needed interactions to reach a successful
recommendation. Under the max entropy strategy, the policy agent is trained to
pick the attribute that reduces the candidate set the most, skipping
attributes with a high disparity in their value distribution. For instance, if
most of the movies in a movie dataset have value Action for the attribute
genre, then the entropy of the attribute genre would be quite low since
knowing the value of it does not help the system gain as much information as
asking about an attribute with lower disparity in its value distribution. By
skipping the attribute genre, the policy agent makes the false assumption that
users desire only the popular Action movies when in fact the user is actually
seeking a Sci-Fi movie.

Besides making false assumptions on specific user preferences, the training of
the policy agent is also affected by the popularity bias introduced by the
recommender component. Since the recommender component learns relatively
uninformative embeddings for unpopular items, the policy agent learns to
elicit more user preferences before making a recommendation. In addition, the
lower magnitude of their learned embeddings makes them rank lower in the
candidate set, and thus they are less likely to be recommended to the user.
Thus, for unpopular items, the agent either exceeds the number of allowed
interactions before making a recommendation or could never successfully
recommend the target items.

Treatment. As discussed above, affected by the max entropy attribute selection
strategy used in pretraining, the policy network tends to make false
assumptions on specific user preferences by skipping attributes with a low
disparity in their value distributions. To tackle this issue, we propose a
dual policy learning scheme. First, we split the entire dataset into two
smaller datasets – $D_{1}$ and $D_{2}$ such that $D_{1}$ contains only the
items with popularity greater than the 80th percentile, and $D_{2}$ contains
only the items with popularity lesser than the 20th percentile. Then, we train
two policy networks, $PN_{1}$ and $PN_{2}$, on $D_{1}$ and $D_{2}$
respectively. When interacting with the user, we select $PN_{1}$ if the item’s
popularity is greater than the 25th percentile and $PN_{2}$ if otherwise. Both
policy networks are first pretrained as classifiers to avoid optimization
failures, then fine-tuned with the standard policy gradient method as the
following:

$\theta\leftarrow\theta-\alpha\nabla log\pi_{\theta}(a^{n}|s^{n})R_{n}$

where $\theta$ denotes the parameter of the policy network, $\alpha$ denotes
the learning rate of the policy network and $R_{n}$ represents the total
accumulated reward from the $n^{th}$ turn to the final turn. Inspired by (Fu
et al., 2021a), we define $R_{n}$ to be:

$R_{n}=\sum_{n=1}^{N}\gamma^{n}(w_{rec}R^{rec}_{n}+w_{conv}R^{conv}_{n}+w_{bias}R^{bias}_{n}),$

where $\gamma$ denotes the discounted factor, $R^{rec}_{n}$, $R^{conv}_{n}$,
and $R^{bias}_{n}$ respectively denotes the recommendation success state, the
user experience of the conversation, and the degree of popularity bias defined
in Equation 2 at the n-th of the interaction. And $w_{rec}$, $w_{conv}$, and
$w_{bias}$ denotes the weight for $R^{rec}_{n}$, $R^{conv}_{n}$, and
$R^{bias}_{n}$ respectively.

The entire framework functions as an entity. The embeddings, learned in the
PAL stage, are used in the CSM stage to reconstruct embeddings for all the
cold-start items. Only with embeddings of all items properly learned, can the
framework train a dual-policy agent that decides the next most appropriate
action to take. To achieve the best performance, the framework must be
executed in the order presented above since each stage has a significant
impact on the following.

##  6\. Experiments

To validate the proposed debiasing framework, we showcase experiments over
real-world datasets designed to answer three key research questions: RQ1. Does
the proposed framework effectively mitigate popularity bias in existing
conversational recommendation methods? RQ2. How does the proposed framework
impact the overall recommendation performance of existing conversational
recommendation methods? RQ3. How does each stage contribute to the mitigation
of popularity bias and impact the overall recommendation performance?

###  6.1. Experiments Setup

Dataset. We conduct experiments on two datasets widely adopted to evaluate
CRSs (Sun and Zhang, 2018; Lei et al., 2020a; Lei et al., 2020b; Xu et al.,
2021b) – Yelp111https://www.yelp.com/dataset/ business recommendation dataset
and Lastfm222https://grouplens.org/datasets/hetrec-2011/ music artist
recommendation dataset. Following previous works (He et al., 2017; Rendle et
al., 2012), we only keep users with at least 10 reviews to alleviate data
sparsity. The user-item interactions are split in the ratio of 7:2:1 for
training, validation, and testing. For the Yelp dataset, we perform the same
hierarchical attribute prepossessing as in previous works (Sun and Zhang,
2018; Lei et al., 2020a; Lei et al., 2020b). The two datasets are summarized
in Table 1.

Table 1. Dataset statistics Dataset | users | items | interactions | attributes
---|---|---|---|---
Yelp | 27,675 | 70,311 | 1,368,606 | 590
Lastfm | 1,801 | 7,432 | 76,693 | 33

Implementation Details. For PAL, we perform grid search to find the best
values for the hyperparameters $n_{1}$ and $n_{2}$. Specifically, we set
$n_{1}$ to 7 and $n_{2}$ to 8. For CSM, we use a two layer neural network with
parameter sizes of $|emb^{onehot}|$ x 128 and 128 x $|emb^{item}|$. For DAL,
each policy network is modeled as a two-layer neural network with parameter
sizes of $|s_{t}|$ x 64 and 64 x $|A|$, where $s_{t}$ denotes the state vector
(see Section 2) and $A$ denotes the action space. we use the REINFORCE
algorithm to train (Williams, 1992) the two policy networks. For calculating
the rewards, we set $R^{rec}$ to 1 if the user accepts the recommendation or
to -1 if the user quits due to impatience or the allowed conversational turns
have been reached. $R^{conv}$ is set to 0.1 if the user replies to the
prompted attribute and to -0.1 if otherwise. $R^{bias}$ is calculated via
Equation 2, and the discount factor $\gamma$ is set to 0.7. We report the best
results of 15 conversational rounds on the Lastfm dataset and 5 conversational
rounds on the Yelp dataset.

Baselines. To examine the proposed framework, we investigate its effectiveness
on the following state-of-the-art baseline conversational recommendation
approaches.

  * $\bullet$

Max Entropy(MaxEnt): MaxEnt is a rule-based attribute selection strategy. At
each turn, the policy agent computes the entropy for each unknown attribute
and selects the attribute with the highest entropy to be the next one to ask.
Recommendation is made either when the candidate space is small enough, or the
policy agent runs out of attributes to ask.

  * $\bullet$

CRM (Sun and Zhang, 2018): CRM is a CRS that uses a belief tracker to record a
user’s preference conveyed during the conversation, and trains a policy
network via reinforcement learning to decide how to interact with the user.
The policy network takes the output of the belief tracker and decides the most
appropriate subsequent action. We follow (Lei et al., 2020a; Lei et al.,
2020b) to adapt it to the multi-round conversational setting to make fair
comparisons.

  * $\bullet$

EAR (Lei et al., 2020a): Similar to CRM, EAR also learns a predictive model to
estimate a user’s preference and trains a policy network to determine whether
to ask more attributes or make recommendations; however, different from CRM,
EAR also considers the feedback from the user to further fine-tune the learned
predictive model, achieving better recommendation performance.

  * $\bullet$

SCPR (Lei et al., 2020b): SCPR is the state-of-the-art multi-round CRS.
Extending from EAR, SCPR leverages the concept of adjacent attributes to
reduce the search space of attributes and builds a knowledge graph to learn a
more efficient policy network.

Besides applying our proposed debiasing framework on the aforementioned CRSs,
we also compare it with Popcorn (Fu et al., 2021a), which focuses on
attribute-based popularity bias in CRSs. We adapt this approach to the
conventional interaction frequency-based popularity definition for a fair
comparison. Note that Popcorn is not a model-agnostic framework and we report
its performance following the workflow in the original paper.

Metrics. To measure the degree of popularity bias in the CRS, we use PER, PSR,
and PCU introduced in Section 4. In addition, since this work intends to
design a framework that mitigates the popularity bias while preserving the
overall recommendation performance, we also include two widely adopted (Sun
and Zhang, 2018; Lei et al., 2020a; Lei et al., 2020b; Xu et al., 2021b)
recommendation performance metrics: success rate (SR@t) and average turns
(AT). Success rate measures if the target item can be successfully recommended
within the allowed turns of interactions while average turns measures the
number of needed interactions to successfully recommend an item. Additionally,
to measure the ratio of successful recommendation for popular and unpopular
items respectively, we also include popular (head) item success rate (HSR) and
unpopular (tail) item success rate (TSR).

###  6.2. Mitigation of Popularity Bias (RQ1)

Table 2. Overall debiasing effectiveness on two benchmark datasets. Popularity correlation with exposure rate (PER$\downarrow$), Popularity correlation with success rate (PSR$\downarrow$), and Popularity correlation with conversational utility (PCU$\downarrow$) are used as evaluation metrics. The best performance is in boldface. | Lastfm | Yelp
---|---|---
| PER | PSR | PCU | PER | PSR | PCU
Popcorn | 3.19 | .448 | .146 | 4.26 | .195 | .255
MaxEnt | 3.99 | .503 | .172 | 2.83 | .110 | .132
w/ Debias | 1.92 | .417 | .077 | 2.29 | .058 | .029
CRM | 4.16 | .474 | .148 | 4.81 | .253 | .347
w/ Debias | 1.24 | .331 | .072 | 3.84 | .103 | .163
EAR | 2.71 | .552 | .224 | 4.99 | .269 | .402
w/ Debias | 1.11 | .318 | .119 | 3.71 | .141 | .170
SCPR | 5.79 | .440 | .281 | 5.97 | .281 | .440
w/ Debias | 3.61 | .322 | .142 | 4.39 | .179 | .257

In this section, we evaluate the debiasing effectiveness of the proposed
framework on each baseline CRS model with different popularity bias metrics
introduced in Section 4. As shown in Table 2, by applying the debiasing
framework on different baseline CRS models, it notably reduces the degree of
popularity bias quantified by all metrics. Meanwhile, the proposed framework
also achieves lower values for PSR and PCU compared to Popcorn on all baseline
models, which further demonstrates its effectiveness in mitigating the
popularity bias in CRSs. On Lastfm, the framework exhibits strong debiasing
performance on all baseline models. For MaxEnt, we observe a lower decrement
in PSR. This is because MaxEnt relies on a rule-based policy agent to select
attributes to ask, which vastly limits the effectiveness of the PAL stage.
However, since MaxEnt is still affected by the issues of biased recommender
and cold-start negligence, applying the CSM stage still dramatically decreases
both PER and PCU. Compared to MaxEnt, the proposed framework demonstrates an
even stronger bias mitigation performance on the RL-based CRS approaches –
CRM, EAR, and SCPR. For CRM, we observe a particularly sharp decrease in PER,
which indicates unpopular items now have higher chances of being recommended
(exposed) to users. For EAR, our framework decreases the PCU by over 68% which
significantly lowers the frequency of unpopular items getting unsuccessfully
recommended due to lengthy conversations. And for SCPR, we observe a 49%
decrease in PCU and a 38% decrease in PER. In addition, our framework
decreases the PSR across EAR, CRM, and SCPR by 30%, 42% and 27% respectively,
significantly weakening the correlation between an item’s popularity and its
recommendation success rate. It is important to note that all baseline models
produce a lesser degree of popularity bias on the Yelp dataset. One key reason
is that the Yelp dataset adopts an enumerated question setting in which a user
can provide values for multiple attributes at each conversational turn.
Compared to the Lastfm dataset, which adopts a binary question setting, the
enumerated question setting facilitates user preference elicitation. As a
result, all items, in general, have higher chances for being successfully
recommended, weakening the correlation between an item’s popularity and its
recommendation success rate, exposure rate, and conversational utility. Even
so, our framework still exhibits strong bias mitigation performance on the
Yelp dataset. In particular, the PSR value for CRM decreases by 59% and the
PCU value for MaxEnt decreases by more than 28%.

Table 3. Overall recommendation performance on two benchmark datasets. Success
rate (SR$\uparrow$), Popular (Head) item success rate (HSR$\uparrow$),
Unpopular (Tail) item success rate (TSR$\uparrow$), and Average Turns
(AT$\downarrow$) are used as evaluation metrics.

| Lastfm | Yelp
---|---|---
| SR | HSR | TSR | AT | SR | HSR | TSR | AT
Popcorn | .396 | .710 | .104 | 12.9 | .637 | .711 | .358 | 3.56
MaxEnt | .286 | .541 | .068 | 13.6 | .507 | .530 | .424 | 3.97
w/ Debias | .311 | .532 | .102 | 13.5 | .509 | .522 | .459 | 3.99
CRM | .324 | .598 | .089 | 13.3 | .604 | .703 | .228 | 3.59
w/ Debias | .438 | .645 | .162 | 12.3 | .633 | .694 | .402 | 3.62
EAR | .421 | .742 | .061 | 12.5 | .629 | .719 | .295 | 3.61
w/ Debias | .479 | .711 | .187 | 12.1 | .647 | .708 | .422 | 3.64
SCPR | .457 | .793 | .109 | 12.5 | .631 | .715 | .301 | 3.67
w/ Debias | .546 | .839 | .216 | 11.5 | .667 | .717 | .501 | 3.75

###  6.3. Recommendation Performance (RQ2)

Since this work intends to mitigate the undesirable effects of popularity bias
while preserving the recommendation performance of the CRS, we also report the
overall recommendation performance on all three baseline CRSs after applying
our framework. In addition to the Recommendation Success Rate (SR) and Average
Turns (AT), we also report the success rate for popular (head) items and the
unpopular (tail) items respectively (HSR and TSR). Following Table 2, we
include the overall recommendation performance of Popcorn (Fu et al., 2021a)
as our baseline and use boldface to denote statistical significance of
$p<0.01$. As shown in Table 3, our framework significantly increases the
overall recommendation performance of all baseline models by greatly
preserving the recommendation success rate for the popular (head) items while
boosting the recommendation success rate for the unpopular (tail) items. In
general, we observe a more minor improvement on the Yelp dataset. This is
because all baseline models already achieve high recommendation performance
due to the enumerated user preference elicitation process. On the contrary, we
observe a higher performance boost across all the baseline models on the
Lastfm dataset. In particular, both the HSR and TSR of CRM increase after
applying the framework. In addition to the DPL stage, the increase in overall
recommendation performance is primarily due to the more effective item
embeddings learned in the PAL stage (for warm-start items) and re-constructed
in the CSM stage (for cold-start items). To compare the effectiveness of the
item embeddings learned with and without the proposed debasing framework, we
calculate their AUC scores on item prediction as in (Rendle et al., 2012). As
shown in Figure 4, the AUC scores of unpopular items significantly increase
across three baseline models on both the the Lastfm and the Yelp dataset. This
is because many of the unpopular items are either cold-start or have extremely
low occurrences in the training set. Re-constructing their embeddings in the
CSM stage increases their effectiveness which in turn increases their
recommendation success rate. In addition, the AUC scores of popular items also
increase on all baseline models, especially for CRM. This is because even
amongst the popular items, there exists a large variation in an item’s
popularity. By strategically re-weighting an item’s relevance based on its
popularity, the PAL stage refrains the recommender from focusing on optimizing
the embeddings for the few extremely popular items, thus learning an overall
more effective set of item embeddings. Note SPCR shares the same recommender
component as EAR, making their learned item embeddings the same; thus we
report its AUC score with EAR.

Figure 4. The performance of Item Prediction Before and After Applying the
Debiasing Framework.

items with higher popularity have greater embedding magnitudes.

###  6.4. Ablation Studies (RQ3)

In this section, we investigate how each stage in the proposed framed work
contributes to the mitigation of popularity bias and the improvement of
recommendation performance via the ablation studies conducted on both EAR and
SCPR with the Lastfm dataset. Note we also perform ablation study on EAR to
serve as a comparison between knowledge-graph based CRS (SCPR) and non-
knowledge-graph based CRS (EAR). For EAR, we find that while the CSM stage
contributes the most to lowering both the PSR and PCU, it also produces a
strong trade-off between the recommendation performance and the debiasing
effectiveness. As shown in Table 5, skipping the CSM stage gives a significant
recommendation performance boost to the system but also limits the overall
bias mitigation effectiveness. Such trade-off happens because since the CSM
reconstructs the embeddings for all cold-start items, the cold-start items
will also predict ratings that reflect a user’s true interests on them, making
them rank significantly higher within the candidate itemset. Therefore, when
the CRS is trying to recommend a popular target item, all the cold-start items
with attributes matching the collected user preferences will now be deemed as
equally qualified as the target item. Consequently, the number of
conversational turns needed to recommend the target item increases, and some
popular items might even fail to get successfully recommended. Interestingly,
skipping the DPL stage also results in a notable increase in PCU which backs
up our intuition of the policy agent prioritizing popular items during the
attribute selection process. Compared to EAR, skipping the DPL stage led to
greater recommendation performance drop in SCPR. This is because SCPR deploys
a knowledge graph that significantly reduces the decision space of the policy
network, which makes our DPL stage more effective. Interestingly for SCPR,
skipping the CSM stage did not lead to better recommendation performance. This
is because the DPL stage functions better on SCPR such that re-constructing
the embeddings for the cold-start items have less impact on the recommendation
performance of the popular items.

Table 4. Debiasing effectiveness and Recommendation Performance of skipping (-) one stage of the proposed debiasing framework on EAR. | PER | PSR | PCU | SR | HSR | TSR | AT
---|---|---|---|---|---|---|---
EAR w/ Debias | 1.11 | .318 | .119 | .479 | .711 | .187 | 12.1
\- PAL | 1.31 | .325 | .166 | .445 | .689 | .171 | 12.4
\- CSM | 1.21 | .439 | .204 | .516 | .814 | .110 | 11.9
\- DPL | 1.23 | .338 | .145 | .459 | .709 | .166 | 12.3
EAR | 2.71 | .552 | .224 | .421 | .742 | .061 | 12.5
Table 5. Debiasing effectiveness and Recommendation Performance of skipping (-) one stage of the proposed debiasing framework on SCPR. | PER | PSR | PCU | SR | HSR | TSR | AT
---|---|---|---|---|---|---|---
SCPR w/ Debias | 3.61 | .322 | .142 | .546 | .839 | .216 | 11.5
\- PAL | 5.05 | .373 | .205 | .458 | .761 | .160 | 12.3
\- CSM | 4.43 | .469 | .275 | .495 | .831 | .097 | 11.9
\- DPL | 4.19 | .422 | .257 | .484 | .805 | .156 | 11.7
SCPR | 5.79 | .440 | .281 | .457 | .793 | .109 | 12.5

##  7\. Related Work

Conversational Recommender Systems. Traditional static recommender systems
that are trained using offline historical user-item interactions face two
inherent challenges: (1) the inability to capture users’ precise preferences;
and (2) failure to provide a human-interpretable justification for their
recommendations (Gao et al., 2021). Although many existing works have
attempted to solve these problems, most rely on a large amount of auxiliary
data to better interpret user intentions. The emergence of CRSs provides an
intrinsic solution to these problems. By dynamically interacting with users
through real-time interactions (e.g., conversations, form fields, buttons and
even gestures (Jannach et al., 2021)), CRSs are able to elicit current and
detailed preferences directly from users, learning precisely what the users’
interests are and thus making highly personalized recommendations that are
justifiable. While early works on CRSs primarily resort to choice-based
questions for collecting user preferences (Graus and Willemsen, 2015; Jiang et
al., 2014), recent works have explored the possibilities of more effective
preference elicitation methods and conversational strategies with the help of
reinforcement learning. For example, (Sun and Zhang, 2018) first proposes a
unified deep RL framework that builds a personalized preference elicitation
process by optimizing a session-based utility function. Inspired by (Sun and
Zhang, 2018), research in (Lei et al., 2020a) further strengthens the
interaction between the recommender component and the conversation component
by proposing a three-stage framework that inherently adapts to a multi-round
setting. Extending upon (Lei et al., 2020a), work proposed in (Lei et al.,
2020b) first integrates a knowledge graph to improve the reasoning ability of
the system and reduce the learning difficulty of the policy agent. In
addition, other research directions in CRSs include dialogue understanding and
generation (Zhang et al., 2018; Zou et al., 2020), response generation (Liu et
al., 2020; Zhou et al., 2020), and the exploration-exploitation trade-offs (Li
et al., 2021; Zhang et al., 2020).

Popularity Bias in Recommender Systems. Many existing works have studied the
impact of popularity bias in traditional static recommender systems. (Steck,
2011) first examines the trade-off between item popularity and recommendation
accuracy. (Park and Tuzhilin, 2008) introduces the importance of long tail
items in promoting user satisfaction and preventing monopoly by big brands.
More comprehensively, (Jannach et al., 2015) empirically demonstrated that
different recommendation algorithms have different vulnerabilities to
popularity bias. To mitigate the harmful effects of popularity bias, a variety
of debiasing approaches have been proposed. (Wei et al., 2021) studies the
popularity bias in recommender systems from a cause-effect perspective and
proposes a model-agnostic counterfactual debiasing method that amends the
learning process of recommendation. And (Wang et al., 2022) proposes to
augment learning for casual users. However, the above introduced works focus
on discovering and mitigating undesirable effects of item popularity in
traditional recommender systems and cannot be directly applied to CRSs.
Recently, (Fu et al., 2021b) introduces the notion of human-in-the-loop (HitL)
reasoning in CRS and (Fu et al., 2021a) first investigates the issue of HitL
bias in CRSs. Specifically, (Fu et al., 2021a) defines a metric that
quantifies the degree of HitL popularity bias, and modifies the reward
function to dynamically mitigate it during the preference elicitation process.
However, it adopts an attribute-based definition of popularity which is less
generalizable and consistent than the conventional interaction-frequency-based
definition. To this end, our work presents a more comprehensive study on
popularity bias in CRSs and proposes a generalizable framework that
interactively mitigates the harmful effects of popularity bias in the entire
system.

##  8\. Conclusion and Future Work

In this work, we present the first systematic study on popularity bias in
conversational recommender systems. We illustrate the existence of popularity
bias in the setting of CRSs from three perspectives. Building upon this
analysis, we propose three metrics for quantifying the degree of popularity
bias in CRSs and a three-staged debiasing framework that can be easily applied
to any CRS. The experimental results on two frequently adopted conversational
recommendation datasets show that this framework not only mitigates the
undesirable effects brought by popularity bias in CRSs but also improves the
overall recommendation performance. In the future, we will explore more
effective mapping schemes for cold-start items.

##  9\. Acknowledgements

This work is supported in part by NSF grant IIS-1939716 and an Amazon Research
Award.

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