Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation

Yi Ren

Xu Zhao
Work done while at Tencent.

Hongyan Tang

Shuai Li

Sequential recommender systems have achieved state-of-the-art recommendation performance by modeling the sequential dynamics of user activities. However, in most recommendation scenarios, the popular items comprise the major part of the previous user actions. Therefore, the learned models are biased towards the popular items irrespective of the user's real interests. Various methods are proposed to address the issue of popularity bias. Traditional methods introduce additional re-ranking steps to augment the coverage of unpopular items or utilize Inverse Propensity Weighting (IPW) to decrease the impact of popular items during model training. Recently, structural causal model-based methods are introduced to achieve superior recommendation performance. Nonetheless, the causal graph proposed before can still be enhanced. Moreover, the design of the existing methods does not leverage the characteristics of sequential model structures, which cannot warrant optimal recommendation performance.

In this paper, we propose a structural causal model-based method to address the popularity bias issue for sequential recommendation model learning. For more generalizable modeling, we disentangle the popularity and interest representations at both the item side and user context side. Based on the disentangled representation, we identify a more effective structural causal graph for general recommendation applications. Then, we design delicate sequential models to apply the aforementioned causal graph to the sequential recommendation scenario for unbiased prediction with counterfactual reasoning. Furthermore, we conduct extensive offline experiments and online A/B tests to verify the proposed DCR (Disentangled Counterfactual Reasoning) method's superior overall performance and understand the effectiveness of the various introduced components. Based on our knowledge, this is the first structural causal model specifically designed for the popularity bias correction of sequential recommendation models, which achieves significant performance gains over the existing methods.

<concept_desc>Information systems Recommender systems</concept_desc>

[500]Information systems Recommender systems

§ INTRODUCTION

We have seen widespread adoption of recommender systems, which can support personalized recommendations for various online services, such as social networking, online media, and e-commerce sites. Among the various recommendation algorithms, sequential recommendation models are increasingly prevalent for a variety of scenarios because of the promising recommendation performance and the effectiveness of user modeling. By explicitly reasoning the dynamic correlations between the user's previous actions and successive behaviors, the sequential models can capture and leverage the useful patterns from sequential dynamics to achieve superior recommendation performance. Based on the underlying network architecture, the sequential models can be roughly classified to RNN based<cit.>, CNN based <cit.> and Self-Attention based <cit.> methods. And all of the aforementioned sequential methods use the user's previous actions as the context so as to accurately predict the next actions.

Most recommendation methods are known to be plagued by the issue of popularity bias <cit.> since the training data is biased towards the popular items. Moreover, the popular items also comprise the major part of the previous user actions, which is the core feature for sequential models. Hence, compared with other recommendation models, the sequential models are more directly affected by such bias and tend to recommend even more popular items without regard to the users' real interests, which can be verified by Figure <ref>.  Based on the previous research <cit.>, over-concentrating on the popular items will hinder the model's accurate understanding of the user's true preference and likely decrease the recommendation diversity and overall utility of the recommendation service.

Comparison of Popularity Bias between SASRec<cit.> and MF<cit.> on the ML-1M dataset. Items are grouped by item popularity in the training set. The left vertical axis denotes the item number ratio of each group. And the right vertical axis represents each group's impression exposure percentage during testing. First, SASRec will expose more items in the '>1k' group than MF. Moreover, compared with the real distribution of the test set, both of them over-concentrate on the '>1k' group.

Owing to the prevalence of recommender systems, the problem of popularity bias negatively impacts the experience of billions of users every day. Hence, significant research efforts <cit.> have been put forward in designing effective debiasing algorithms to alleviate the aforementioned issues. First, some methods <cit.> introduces additional post-processing and re-ranking steps to increase the exposure of unpopular items and balance the recommendation diversity and utility. Another line of works <cit.> resort to rebalancing the loss weight of different training samples with the inverse propensity weighting (IPW). Although theoretically sound, these methods are not robust to small propensities as small propensities often lead to extremely large sample weights and uncontrollable high variance. Some methods <cit.> are proposed to practically control the variance and alleviate the instability issue at the cost of introducing additional bias. However, the bias-variance trade-off makes the model hard to tune to achieve satisfactory results.

Recently, structural causal model-based methods <cit.> are proposed to further advance the debiasing methods for superior performance. The confounding and colliding structures<cit.> usually exist in a causal graph reflecting real-world relations of variables. With such unfavorable structures, it is impossible to answer the causal question with correlation-level tools since confounders and controlling colliders bring spurious correlations between treatment and outcome. To cope with this challenge, the structural causal model provides both solid mathematical foundations and friendly calculus for the analysis of causes and counterfactuals. Therefore, it can help to deduce the direct and indirect effects of potential interventions in the counterfactual world. For example, MACR<cit.> proposes the framework to counteract popularity bias by carrying out causal reasoning in the counterfactual world. And DICE<cit.> assigns users separate embeddings for interest and conformity to derive generalizable representations by leveraging the colliding structures.  The latest experiment results also prove that these methods can achieve state-of-the-art recommendation performance. However, the utilized causal graph can still be improved. Furthermore, the existing structural causal model-based methods are not specifically designed for the sequential recommendation scenario, which cannot warrant optimal recommendation results in the sequential recommendation setting.

Accordingly, in this paper, a structural causal model-based method is specifically designed for the sequential recommendation setting to address the popularity bias issue much more effectively. First, we explicitly disentangle the item embeddings to popularity and interest representations and disentangle user preference to conformity and interest representations for generalizable modeling. Second, with the disentangled representations, we identify a fine-grained structural causal graph for general recommenders. Within this causal graph, the popularity and conformity representations exert a direct effect on user interactions. For the indirect effect, the popularity-conformity embeddings and interest embeddings are matched separately and then combined together so as to model to what degree the user interaction is from the match to user interest or conformity. Moreover, considering the superior performance of sequential recommender models, we apply the proposed causal graph to the sequential recommendation and specifically design effective sequential network structures so as to perform counterfactual reasoning for alleviating the popularity bias. Finally, extensive experiments are conducted to verify that this method can achieve superior overall recommendation performance compared with the existing methods. We also perform detailed ablation tests to study the benefit and effectiveness of the various introduced components.

Compared with MACR<cit.>, our model utilizes disentangled representations, which can warrant more generalizable models. In comparison with DICE<cit.>, our disentangling method is much simpler and does not rely on sophisticated data preparation for curriculum learning. And we leverage counterfactual reasoning, which is absent with DICE, for unbiased inference. Furthermore, for accurate bias correction, we utilize separate sequential structures to process the interest and popularity representations of historical interactions to model the user's preference for interest and conformtiy respectively.
We summarize our main contributions as follows.

* By leveraging disentangled user and item representations, we present a new causal view of the popularity bias in general recommender systems. Disentangled representations contribute to more generalizable and robust modeling<cit.>.

* We apply this causal graph to the sequential recommendation scenario. For accurate bias correction, we design separate sequential structures based on the interest or popularity representations of historical interactions to precisely model the user's preference for interest or conformtiy.

* Thanks to the proposed approach, counterfactual reasoning can be performed to compensate for the popularity bias with proper operation in the training and inference stage.

* We conduct extensive experiments with multiple real-world datasets over RNN-based, CNN-based, and Self-Attention-based backbone networks to verify and understand the effectiveness of the overall method and each individual component.

* We perform online A/B testing to prove the method can be practically applied in industry recommender systems to enhance the utility of the recommendation service.

§ RELATED WORKS

§.§ Sequential Recommendation

Traditional recommendation models discard any sequential information and generate recommendations by learning the user's general interests. As a result, their recommendation performance can be limited without considering the rich contextual information of user action histories. Various sequential recommendation models has been proposed before to provide superior recommendation results. Early works on sequential recommendation utilizes Markov chains <cit.> to capture typical sequential patterns of user historical actions. With the advance of deep learning technologies, deep learning models, including RNN based, CNN based and attention based methods, achieve the state-of-the-art recommendation performance.

RNN based models <cit.> leverage RNN or its variants, such as Gated Recurrent Unit (GRU)<cit.> and Long Short-Term Memory (LSTM)<cit.>, to encode the user's previous actions into a compressed vector. With this vector as a context, these models are able to capture the dynamic and evolving interests of different users. Though many methods <cit.> with different loss functions and sampling strategies are proposed, they all belong to this category. For the problem of sequential recommendation, some adjacent actions may not have dependency relation (e.g. a user bought $i_3$ because of $i_0$ rather than $i_2$), which is inconsistent with the assumptions for model structure design of RNN. Moreover, because of the size limitation of the compressed vector, these models may have trouble to model long range impact among user actions.

Admittedly, CNN based architectures are not a natural way to capture sequential patterns. But Tang and Wang manage to propose a competitive Convolutional Sequence Model (Caser) <cit.> to capture sequential patterns at point-level, union-level and skip behaviors with both horizontal and vertical convolutional filters. And NextItNet <cit.> further improves the recommendation performance by addressing some design limitations of Caser.

As self-attention mechanism has shown promising results for sequential data <cit.>, we see rising enthusiasm to apply it to sequential recommendation. Kang et al. propose SASRec <cit.> consisting of multi-layer transformer decoders to model user's sequential actions and achieve state-of-the-art performance on multiple datasets. Besides SASRec, with the introduction of Cloze task, BERT4Rec <cit.> utilizes bidirectional self-attention networks to capture the pattern of user behaviors. Generally speaking, the self-attention based models can achieve supeior performance compared with the previous models.

§.§ Existing Debias Methods for Popularity Bias

Since the training data for recommender systems is collected from observational results rather than controlled experiments, the interaction data is missing-not-at-random and the popular items are over-represented in the training samples. Therefore, the recommender model will learn skewed user preference with the impact of popularity bias. As popularity bias is an important problem for recommender systems, it arouses rising interests from many researchers. To counteract the adverse effect of popularity bias, the existing methods can be roughly classified to three categories, namely post-processing with re-ranking, Inverse Propensity Weighting and Structural Causal models.

§.§.§ Re-Ranking

These methods post-process the ranking model's results to balance the exposure rate of items from different popularity levels. Himan et al. <cit.> increase the exposure of less popular items in recommendations with the application of a personalized diversification re-ranking approach. Moreover, Zhu <cit.> proposes an effective re-ranking methods to ensure fair exposure of items with equal user preference match. Nonetheless, these approaches usually promote diversity at the cost of the overall utility metrics.

§.§.§ Inverse Propensity Weighting

Multiple methods are proposed to learn unbiased models by utilizing inverse propensity weighting <cit.>  to adjust the weight of different training samples. Rel-MF <cit.> derives the unbiased point-wise loss for enhanced recommendation qualtity. And MF-DU <cit.> achieves better bias correction by separately estimating the exposure probability for interacted and non-interacted data. For the pair-wise learning setting, Unbiased Bayesian Personalized Ranking <cit.> extends the algorithm of BPR<cit.> and formulates an unbiased objective function. Recently, Wang et al. <cit.> proposes a bias correction approach for sequential recommendation. But to estimate the propensity with his approach, it can only work for the problem of predicting whether the user will positively rate the interacted items rather than the usual setting of estimating the probability of the next interaction item for sequential recommendation. These IPW related methods achieve success in many applications. However, their performance may be adversely affected by the high variance <cit.> problem.

§.§.§ Structural Causal Model

Structural causal model-based methods <cit.> achieve the state-of-the-art debiasing performance. By effectively modeling the confounding and colliding causal graph structures reflecting real-world relations, they are able to predict the direct and indirect effects of counterfactual interventions. DICE <cit.> disentangles the user embedding to represent conformity and interest separately for causal recommendation. MACR <cit.> proposes a model-agnostic counterfactual reasoning (MACR) framework and performs counterfactual inference to eliminate the impact of popularity bias. Compared with the previous two algorithms, PDA <cit.> and $CD^2AN$ <cit.> works in a slightly different setting by addressing the popularity drift issue (The item's popularity tends to vary with time). They estimate the item's future popularity and further leverage it to attain superior performance. In their setting, the data is separated based on different time slot. And they use the last slot's data rather than unbiased data for offline testing in their experiments.

§ PROBLEM DEFINITION

For the sequential recommendation problem, there exist a set of users ($\mathcal{U} = \{u_i\}_{1 \leq i \leq N}$) and a candidate set of items ($\mathcal{I} = \{i_j\}_{1 \leq j \leq M}$). Moreover, each user $u$'s interaction history consists of a sequence of items from $\mathcal{I}$, $S^u=(s_1^u, s_2^u,...,s_{t-1}^u)$, where $s_j^u \in \mathcal{I}$. The index $j$ for $s_j^u$ denotes the step in which an action occurs in the interaction sequence. Given the current interaction history of $S^u$, sequential recommendation intends to predict the item that user $u$ will interact with at the next time step $t$. To attain the goal, the recommender model usually model the interaction probability over all possible items with point-wise loss function or estimate the relative order of different item pairs with pair-wise loss function <cit.>.

Popular items are exposed much more frequently than expected <cit.>. Because of the skewed training data, all of the recommendation models are affected by the popularity bias <cit.> and may fail to recommend unpopular items, which match the user's interest better. Moreover, the popular items constitute the major part of the user's interaction sequence, which is an essential feature for sequential recommendation. Therefore, compared with other general recommendation models, the popularity bias <cit.> impacts the performance of sequential models more severely, which can be proven by Figure <ref>. Consequently, it is highly desirable to apply effective debiasing methods to understand the user's preferences well for better recommendation performance. To assess the real recommendation quality, we follow the practice of prior works <cit.> and perform unbiased evaluation with re-weighted testing data that is uniform distribution over items.

§ METHODOLOGY

In this section, we firstly present the causal graph and how to carry out counterfactual reasoning based on it for unbiased preference learning. Please note that this causal graph can apply to general recommendation models. In this paper, we concentrate on sequential models mainly because of their effectiveness and superior performance. Second, the general design of base sequential models are described in detail. Finally, we elaborate on how to apply the proposed causal graph to sequential recommendation by specifically augmenting the base sequential model structures and loss functions. For accurate bias correction, we design separate sequential structures based on the interest or popularity representations of historical interactions to precisely model the user's preference for interest or conformtiy. We also discuss why the proposed approach can effectively address the popularity bias issue.

[Real World]

[Counterfactual World]

Comparison between real world and counterfactual
world causal graphs

Model Architecture

§.§ Causal Graph for Counterfactual Reasoning

Each causal graph <cit.> is an instance of directed acyclic graph (DAG) $G=\{V,E\}$, where $V$ denotes the set of variables and $E$ represents the cause-effect relations among these variables. In the causal graph, the variables are represented with capital letters. And lowercase letter denotes an observed value for certain variable. There are both direct effects and indirect effects in the causal graph. For instance, in Figure <ref>, $I^{pop}$ exerts both direct effects (through the path of $I^{pop} \rightarrow Y$) and indirect effects (through $I^{pop} \rightarrow M^{con} \rightarrow M \rightarrow Y$) on $Y$.

Please refer to Figure <ref> for the proposed causal graph. $U$ and $I$ denote the user and item embeddings respectively. First, for user embedding, we can further refine it to two representations to denote the user's preference for conformity and interest. In parallel, the item embedding can be refined to interest representations and popularity representations. Second, $M^{con}$ denotes the match between user conformity and item popularity. We use vectorized representations rather than scalar values to capture the diversity in user's varying conformity behaviors for different category items. Moreover, the match score $M$ is derived based on $M^{con}$ and $M^{int}$ (The interest match between user and item). The explicit modeling on the effect (interaction) and cause (conformity or interest) leads to robust models with stronger generalization capabilities <cit.>, especially when the training and testing data are collected with different causes (conformity or interest).

Besides the match score between the user and item, $I^{pop}$ and $U^{con}$ have direct impact on interaction $Y$. Popular items are exposed more by the existing recommender systems and interacted more by the users. And high conformity "easy" users tend to interact irrespective of his true preference. Unlike popularity and conformity, it is reasonable to suppose the interest representations only impact $Y$ through the match between the corresponding user and item. As item popularity directly impact $Y$, the model will assign unreasonable higher scores for popular items and recommend them more, thereby rendering the adverse feedback loop. In principal, removing the direct effect from item popularity and user conformity can help to eliminate the popularity bias, which means to mask the impact from the paths of $I \rightarrow I^{pop} \rightarrow Y$ and $U \rightarrow U^{con} \rightarrow Y$

Given a specific user item pair ($u, i$), let $U^{con}_u$, $I^{pop}_i$ and $M_{u,i}$ denote the conformity representation for $u$, popularity representation for $i$ and the overall match score for ($u,i$) pair. Then, the final prediction score can be represented with $Y_{U^{con}_u,I^{pop}_i,M_{u,i}}$.

Reasoning in the counterfactual world (Figure <ref>) is necessary to estimate the unbiased user preference. In this counterfactual world, for the path of $U \rightarrow U^{con} \rightarrow Y$ and $I \rightarrow I^{pop} \rightarrow Y$, the previous user item pair ($u, i$) is still used. However, the match score $M_{u^*,i^*}$ is computed based on the pair of ($u^*,i^*$) where the reference status of $I=i^*$ and $U=u^*$ refer to the situation of masking $I$ and $U$ from real world as null or taking average values. Please note that $U$ and $I$ have different input values for varying paths, which can never happen in an real world scenario. In this counterfactual world, it is straightforward to derive the final prediction score as $Y_{U^{con}_u,I^{pop}_i,M_{u^*,i^*}}$.

Accordingly, the unbiased preference can be obtained as follows:

unbiased_score = Y_U^con_u,I^pop_i,M_u,i - Y_U^con_u,I^pop_i,M_u^*,i^*

which means the impact of $U$ and $I$ on $Y$ only through the paths of $I \rightarrow I^{int},I^{pop} \rightarrow M^{con},M^{int} \rightarrow M \rightarrow Y$ and $U \rightarrow U^{int},U^{con} \rightarrow M^{con},M^{int} \rightarrow M \rightarrow Y$.

§.§ Base Sequential Models

Please refer to Figure <ref> (a) for the architecture of a general sequential recommendation model. These blocks in blue represent trainable components. Generally speaking, a typical sequential model takes three types of features as input, namely item features, user interaction sequence feature and other user features (e.g., user id and location). Usually, all of these features are high-dimensional binary features from one-hot encoding of categorical variables (e.g. user and item id) or discretization of dense variables (e.g., activity counting features). First, these features are projected to dense representations with the fully connected embedding layers.

e_u = Embedding_u(u)

e_i = Embedding_i(i)

e_s_1,e_s_2,...,e_s_t-1 = Embedding_seq(s_1^u, s_2^u,...,s_t-1^u)

where $Embedding_{seq}$ and $Embedding_i$ usually share the same parameters to boost the recommendation performance <cit.>. And $e_u \in R^{1 \times d}$, $e_i \in R^{1 \times d}$ and $e_{s_j} \in R^{1 \times d}$ denote the $d$ dimensional user, item and sequence embeddings.

Then, a state-of-the-art sequential network, such as RNN<cit.>, CNN<cit.> or Transformer<cit.>, accepts the sequence embedding $(e_{s1},e_{s2},...,e_{sk}$) and generates the user's dynamic preference.

pref^dyn_u,t = Seq(e_s_1,e_s_2,...,e_s_t-1)

where $pref^{dyn}_{u,t}$ means the dynamic preference of $u$ at step $t$.

Moreover, to get accurate overall user preference, we can merge the information from $pref^{dyn}_{u,t}$ and $e_u$. Please note that this operation can involve a trainable network, a simple concatenation or just return $pref^{dyn}_{u,t}$ itself. Based on our experiments, we can achieve competitive results with $pref^{dyn}_{u,t}$ itself on the public datasets, which is consistent with the results of previous works <cit.>. In this case, the explicit user embeddings are actually not used. For our industry data, we observe consistent gains by merging together these two representations of $pref^{dyn}_{u,t}$ and $e_u$.

pref_u,t = Merge(e_u, pref^dyn_u,t)

Finally, with $pref_{u,t}$ and $e_i$ as inputs, the prediction score for step ($t$) can be estimated by the match network. This network can be a MLP or just dot product.

ŷ_u,i,t = Match(e_i, pref_u,t)

For model training, the point-wise loss is calculated based on the match score and the corresponding training label. For pair-wise loss <cit.>, we need to compute the loss based on a user's match scores with different target items.

For the results of equation (<ref>) to (<ref>), all of them are vectors. And the outcome of equation (<ref>) is a scalar value.

§.§ Proposed Model Architecture of DCR

In this section, we describe the overall proposed model architecture. We will discuss how to counteract popularity bias with proper model training and inference in the following sections. For the results of equation (<ref>) to (<ref>), all of the values are vectors. And the outcomes of equation (<ref>) to (<ref>) to are of scalar values.

§.§.§ Representation Disentanglement

As illustrated in Figure <ref> (b), the proposed model accepts the same set of features and leverages Equations (<ref>),(<ref>),(<ref>) to derive $e_u$, $e_i$ and the sequence embeddings of $e_{s_1},e_{s_2},...,e_{s_{t-1}}$. Then, we disentangle the embeddings of the target item and items in the user's historical interaction sequence with shared encoder networks to interest representations and popularity representations. In parallel, we disentangle the user embedding to interest representations and conformity representations. Since an overview of the whole picture is very helpful for understanding further design decisions, we delay the discussion of how to ensure high quality representation disentanglement with the introduction of well-designed training losses to the next sections. To represent item popularity and user conformity well, we do not think scalar values suffice. For example, the popularity level of items from different categories tend to have very different impact on interaction probabilities with the same user. As a result, we maintain vectorized popularity and conformity representations for fine-grained modeling. There operations can be formally defined below.

e^con_u = Conformity_u(e_u)

e^int_u = Interest_u(e_u)

e^pop_i = Popularity_i(e_i)

e^int_i = Interest_i(e_i)

e^pop_s_j = Popularity_seq(e_s_j)_1 ≤j ≤t-1

e^int_s_j = Interest_seq(e_s_j)_1 ≤j ≤t-1

where $Popularity_i$ and $Interest_i$ share the same parameters with $Popularity_{seq}$ and $Interest_{seq}$ correspondingly. And each equation denotes a trainable neural network module.

Please note that $e^{pop}_i$ and $e^{int}_i$ correspond to the implementation for the nodes of $I^{pop}$ and $I^{int}$ in Figure <ref> respectively.

§.§.§ User Preference Mining

Then, separate sequential networks are leveraged to mine the user's dynamic preference for interest and conformity from the disentangled embeddings of the interaction item sequence. Specifically, we think the sequence of interest embeddings of historical interacted items can help to represent the user's interests. And we further argue that the user's conformity can be mined from $e^{pop}_{s_1},e^{pop}_{s_2},...,e^{pop}_{s_{t-1}}$. Additionally, we also merge with the disentangled representations from $e_u$ to derive the user's next step preference for interest and conformity. The results of equation (<ref>) and (<ref>), namely $pref^{con}_{u,t}$ and $pref^{int}_{u,t}$,  stand for the implementation for the nodes of $U^{con}$ and $U^{int}$ in Figure <ref> respectively.

pref^con_u,t = Merge^con(Seq^con(e^pop_s_1,e^pop_s_2,...,e^pop_s_t-1),e^con_u)

pref^int_u,t = Merge^int(Seq^int(e^int_s_1,e^int_s_2,...,e^int_s_t-1),e^int_u)

where $Seq^{con}$ and $Seq^{int}$ can be any sequential networks, including RNN<cit.>, CNN<cit.> and Self-Attention<cit.>. And the merge operation can involve a trainable network, simple concatenation or just
emitting one of the inputs.

§.§.§ User Item Matching

At the next step, besides the interest match network, a standalone network is utilized to compute the match score between user conformity and item popularity. Moreover, we compute dynamic weights with MLP for the match scores of user interest and conformity with equation (<ref>) to capture their varying importance for different contexts. Finally, we can get the match score $\hat{y}_{m,u,i,t}$ for step $t$ with equation (<ref>).

ŷ^con_m,u,i,t = Match^con(e^pop_i, pref^con_u,t)

ŷ^int_m,u,i,t = Match^int(e^int_i, pref^int_u,t)

w^int = σ(AttenNet(e^int_i, pref^int_u,t, e^pop_i, pref^con_u,t))

ŷ_m,u,i,t = w^int * ŷ^int_m,u,i,t + (1.0 - w^int) * ŷ^con_m,u,i,t

where $\hat{y}^{con}_{m,u,i,t}$, $\hat{y}^{int}_{m,u,i,t}$ and $\hat{y}_{m,u,i,t}$ denote the implementation for the nodes of $M^{con}$, $M^{int}$ and $M$ in Figure <ref> respectively. And $\sigma$ stands for the sigmoid function. Moreover, the match function can involve a trainable MLP network or dot product operation.

§.§.§ Incorporating Direct Effect of User Conformity and Item Popularity

Based on section <ref>, besides the match score between user and item, we also need introduce the direct effect from user conformity and item popularity. First, we define influence from item popularity as below.

ŷ_i = ItemPopularity(e^pop_i)

Furthermore, we represent the extent to which the user would interact with items regardless of preference match as follows.

ŷ_u,t = UserConformity(pref^con_u,t)

Finally, these two scores are aggregated with the match score to acquire the final prediction score for step $k+1$ as below.

ŷ_u,i,t = ŷ_m,u,i,t * σ(ŷ_u,t) * σ(ŷ_i)

where $\hat{y}_{u,i,t}$ denotes the implementation for the node $Y$ in Figure <ref>.

With this design, to generate high score for the interaction between a non-conformitive user and an unpopular item, the score of $\hat{y}_{m,u,i,t}$ must be pushed high during model training.

§.§ DCR Model Training

First, following <cit.>, we take $\hat{y}_{u,i,t}$ from equation (<ref>) as prediction and define the main loss with binary cross entropy (BCE) loss <cit.> in equation (<ref>).

L_main = -∑_(u,i,t) ∈D y_u,i,t  ln(σ(ŷ_u,i,t))+(1-y_u,i,t)ln(1-σ(ŷ_u,i,t))

where $\sigma$ means the sigmoid function and $y_{u,i,t}$ is the ground truth label for step $t$. In our experiments, we will verify our proposed method also achieves substantial gains over the existing debiasing methods with pair-wise loss functions<cit.>.

Second, to make sure $e^{pop}_i$ contains the desired item popularity information, additional supervision is added for $\hat{y}_i$ from equation (<ref>). As item popularity directly impacts interaction, we add the BCE loss for $\hat{y}_i$ below.

L_i = -∑_(u,i,t) ∈D y_u,i,t  ln(σ(ŷ_i))+(1-y_u,i,t)  ln(1-σ(ŷ_i))

Unlike MACR<cit.>, we do not introduce the loss for the user tower as we find this loss has marginal impact on model performance, which is consistent with MACR's results.

Moreover, we also add supervision for $\hat{y}^{con}_{m,u,i,t}$ from equation (<ref>) and $\hat{y}^{int}_{m,u,i,t}$ from (<ref>). As $\hat{y}^{int}_{m,u,i,t}$ should capture the user's unbiased interests, we leverage the proposed BCE based IPW loss proposed in <cit.>, which achieves superior performance among the point-wise IPW methods. Specifically, they propose to define propensity scores for positive and negative items separately as below.

θ_*,i^+=(n_i/max_i ∈In_i)^ω      θ_*,i^-=(1-n_i/max_i ∈In_i)^ρ

where $n_i$ signifies the number of interactions to the item $i$ by all users. And $\omega,\rho \in [0,1]$, which are both empirically set to 0.5 in their work, are the hyperparameters to control the skewness of item popularity. Then, we can define the supervision for $\hat{y}^{int}_{m,u,i,t}$ as below. Though IPW loss has the large variance issue, the impact here is much lighter as the auxiliary loss usually uses a much smaller weight than the main loss.

L^int_m = -∑_(u,i,t) ∈D y_u,i,t/θ_*,i^+ln(σ(ŷ^int_m,u,i,t))+(1-y_u,i,t)/θ_*,i^-ln(1-σ(ŷ^int_m,u,i,t))

As $\hat{y}^{con}_{m,u,i,t}$ should also be able to explain the existing interactions. Similar to $\hat{y}_i$, we add the BCE loss below. With $L^{con}_{m}$, in order to match well with $e^{pop}_i$, $pref^{con}_{u,t}$ should approximate to the user's conformity.

L^con_m = -∑_(u,i,t) ∈D y_u,i,t  ln(σ(ŷ^con_m,u,i,t))+(1-y_u,i,t)  ln(1-σ(ŷ^con_m,u,i,t))

$pref^{int}_{u,t}$ from (<ref>), $e^{int}_u$ and $e^{int}_i$ should approximate to the unbiased interest representations owing to the IPW loss of $L^{int}_m$. Moreover, with the aforementioned auxiliary loss of $L_i$ and $L^{con}_m$, $pref^{con}_{u,t}$ from (<ref>), $e^{con}_u$ and $e^{pop}_i$ are forced to contain conformity and popularity information. But $pref^{con}_{u,t}$, $e^{con}_u$ and $e^{pop}_i$ may still undesirably contain some interest information in theory. For proper representation disentanglement, $e^{con}_u$ and $e^{int}_u$ are enforced to be  independent with orthogonality loss. And the same loss is applied to $e^{pop}_i$ and $e^{int}_i$. We list the two orthogonality loss as below.

L^ortho_u = |CosineSimilarity(e^con_u, e^int_u)|

L^ortho_i = |CosineSimilarity(e^pop_i, e^int_i)|

Finally, we can leverage the multi-task learning schema and use the loss below to optimize the proposed model.

L = L_main + α(L^int_m + L^con_m) + βL_i + γ(L^ortho_u + L^ortho_i)

where $\alpha$, $\beta$ and $\gamma$ are the hyper-parameters to be tuned. We will carry out well-designed sensitivity tests to investigate their impact.

§.§ DCR Model Inference

As aforementioned in section <ref>, the popularity bias can be eliminated if we can remove the effect of the path $U -> U^{con} -> Y$ and $I -> I^{pop} -> Y$. For unbiased recommendation, we propose to adjust the score $\hat{y}_{u,i,t}$ from equation (<ref>) for ranking as below:

ŷ_u,i,t - c * σ(ŷ_u,t) * σ(ŷ_i)

where $c$ represents the reference status of $\hat{y}_{m,u,i,t}$ from eq (<ref>), namely the $M$ node in Figure <ref>.

The above inference is an exact implementation for equation (<ref>). $\hat{y}_{u,i,t}$ denotes the score from the real world while $c * \sigma(\hat{y}_{u,t}) * \sigma(\hat{y}_{i})$ acts as the score from the counterfactual world. With the help of counterfactual reasoning and disentangled representations, the recommendation model can attain superior recommendation results.

§ EXPERIMENTS

The dataset statistics for user count, item count, interaction count and gini index <cit.> computed from item occurrence frequency.

MovieLens-1M     Video Games     Steam

User Count            6,040             31,013           334,730

Item Count            3,416             23,715           13,047

Interaction Count     999,611           287,107          3,686,172

Item Gini Index       0.6036           0.6567          0.8398

The performance evaluation of the compared methods with NDCG@10 and Hit Rate@10. The bold-face font denotes the winner in that row. The best results with "*" symbol indicate p < 0.05 for one-tailed t-test with the best competitor. Moreover, the underline symbol denotes the best results excluding DCR.

SeqNet                         Data                       Metric      $Base_{bce}$     $Base_{bpr}$     $\makecell[c]{Bias\\Tower}$     $IPW_{bce}$     $IPW_{bpr}$          $DICE$             $PD$     $MACR$             $\makecell[c]{DCR\\var0}$     $\makecell[c]{DCR\\var1}$     $\makecell[c]{DCR\\var2}$     $DCR$               $RelGain$

6*SASRec<cit.>        2*ML-1M     NDCG        0.3732         0.3704         0.3993                          0.3931        0.3946             0.3858             0.3842             0.4077     0.4137                        0.4171                        0.4325                        $\boldsymbol{0.4438}^*$     8.85%

HitRate     0.5725         0.5715         0.6011                          0.5911        0.5902             0.5888             0.5830             0.6208     0.6279                        0.6207                        0.6427                        $\boldsymbol{0.6680}^*$     7.60%

2*Games     NDCG        0.2962         0.3088         0.3054                          0.2971        0.3175     0.2968             0.3145             0.2954             0.3022                        0.3081                        0.3174                        $\boldsymbol{0.3217}^*$     1.32%

HitRate     0.4693         0.4961         0.4794                          0.4717        0.5019     0.4894             0.4856             0.4692             0.4789                        0.4808                        0.4979                        $\boldsymbol{0.5059}$       0.80%

2*Steam     NDCG        0.1989         0.1927         0.2133                          0.2023        0.2138             0.2369     0.2142             0.2216             0.2297                        0.2402                        0.2441                        $\boldsymbol{0.2556}^*$     7.89%

HitRate     0.3446         0.3389         0.3683                          0.3453        0.3597             0.3946     0.3642             0.3723             0.3995                        0.3978                        0.4057                        $\boldsymbol{0.4304}^*$     9.07%

6*GRU4Rec<cit.>       2*ML-1M     NDCG        0.3586         0.3565         0.3783                          0.3749        0.3797             0.3738             0.3763             0.3878     0.4043                        0.4078                        0.4190                        $\boldsymbol{0.4346}^*$     12.07%

HitRate     0.5525         0.5501         0.5724                          0.5685        0.5730             0.5668             0.5717             0.5984     0.6135                        0.6095                        0.6263                        $\boldsymbol{0.6487}^*$     8.41%

2*Games     NDCG        0.2315         0.2529         0.2289                          0.2292        0.2597     0.2413             0.2533             0.2389             0.2264                        0.2448                        0.2575                        $\boldsymbol{0.2636}^*$     1.50%

HitRate     0.3860         0.4259         0.3924                          0.3854        0.4292     0.4169             0.4148             0.4006             0.3832                        0.4012                        0.4279                        $\boldsymbol{0.4337}$       1.05%

2*Steam     NDCG        0.1640         0.1637         0.1661                          0.1554        0.1784             0.2099     0.1779             0.1769             0.1918                        0.2057                        0.2078                        $\boldsymbol{0.2210}^*$     5.29%

HitRate     0.2917         0.2966         0.2969                          0.2734        0.3103             0.3560     0.3169             0.3096             0.3426                        0.3446                        0.3487                        $\boldsymbol{0.3758}^*$     5.56%

6*NextItNet<cit.>     2*ML-1M     NDCG        0.3560         0.3469         0.3778                          0.3673        0.3748             0.3739             0.3637             0.3855     0.3641                        0.3922                        0.3953                        $\boldsymbol{0.4124}^*$     6.98%

HitRate     0.5463         0.5421         0.5706                          0.5651        0.5687             0.5683             0.5509             0.6002     0.5732                        0.5902                        0.6097                        $\boldsymbol{0.6317}^*$     5.25%

2*Games     NDCG        0.2413         0.2580         0.2498                          0.2306        0.2682             0.2440             0.2697     0.2421             0.2496                        0.2684                        0.2828                        $\boldsymbol{0.2896}^*$     7.38%

HitRate     0.3993         0.4341         0.4175                          0.3902        0.4456     0.4241             0.4308             0.4002             0.4211                        0.4364                        0.4605                        $\boldsymbol{0.4636}^*$     4.04%

2*Steam     NDCG        0.1686         0.1625         0.1743                          0.1618        0.1759             0.2047     0.1792             0.1795             0.1877                        0.2067                        0.2064                        $\boldsymbol{0.2162}^*$     5.62%

HitRate     0.2979         0.2927         0.3049                          0.2835        0.3043             0.3463     0.3164             0.3152             0.3363                        0.3479                        0.3562                        $\boldsymbol{0.3744}^*$     8.11%

The performance evaluation of tuning $\alpha$ and $\beta$ on MovieLens-1M

2*Backbone     2*[c]Loss Weight
to tune     2c|2e-4     2c|2e-3     2c|2e-2             2c|2e-1     2c2e0

NDCG       Hit Rate             NDCG       Hit Rate             NDCG                Hit Rate            NDCG       Hit Rate             NDCG       Hit Rate

$\alpha$                            0.4265     0.6319               0.4223     0.6317               0.4438     0.6680     0.4155     0.6565               0.4117     0.6497

$\beta$                             0.3168     0.5128               0.3570     0.5617               0.4438     0.6680     0.3877     0.6221               0.3086     0.5076

$\alpha$                            0.4049     0.6209               0.4073     0.6113               0.4346     0.6487     0.4180     0.6431               0.4015     0.6264

$\beta$                             0.2692     0.4530               0.3324     0.5316               0.4346     0.6487     0.3698     0.5899               0.3101     0.5023

In this section, we conduct extensive experiments with the aim of answering the following questions:

RQ1 Compared with the existing debiasing methods, can DCR achieve significant gains for a variety of base sequential models on multiple datasets ?

RQ2 Can we verify the effectiveness and necessity of the introduced individual components?

RQ3 How sensitive do the multiple hyper-parameters, including $\alpha$, $\beta$, $\gamma$ and $c$, impact the overall recommendation quality?

RQ4 Can DCR achieve online gains over the existing state of the art debiasing methods if deployed on industrial recommendation service ?

§.§ Offline Experimental Settings

§.§.§ Datasets

We evaluate our methods on three public benchmark datasets, whose statistics are shown in Table <ref>.

* MovieLens-1M<cit.>: One of the currently released MovieLens datasets, which contains 1 million movie ratings from about six thousand users across more than 3,000 movies.

* Amazon<cit.>: A series of datasets consisting of product reviews from Amazon.com. We use the sub-category of "Video Games".

* Steam<cit.>: A dataset crawled from an online game distribution platform named Steam. It includes game reviews and some other useful information, such as developer, category and pricing.

From the gini index <cit.> computed based on item occurrence frequency, we can apparently observe the existence of popularity bias in these datasets. We follow the same pre-processing procedure as <cit.> and discard rare users and items with lower than 5 related interactions. For each user, we treat the presence of a review or rating as implicit feedback and sort chronologically to decide the sequence order. For data partitioning, we treat the most recent action of each user ($S^u_{|S^u|}$) as testing. And the second most recent action ($S^u_{|S^u|-1}$) is left for validation.  All the previous items at $S^u$ are for training.

§.§.§ Experimental Models

To verify the effectiveness, we conduct experiments on three popular sequential networks, including RNN based<cit.>, CNN based<cit.> and Self-Attention based<cit.>. Based on our tests, adding explicit user embeddings does not achieve significant performance gains for the public data, which is consistent with <cit.>. Thus, by default, the user preference merge networks at Figure <ref> just copy the results of the sequential networks. But with specific experiment on the public data and the industrial model deployed for online A/B testing, DCR shows good performance for the configuration with explicit user embeddings. For the match network in Figure <ref>, we use simple dot product as we do not observe gains with complex MLP networks. For the embedding disentanglement networks, we use two layer MLP structure of (150, 50) for interest while (100, 50) for popularity and conformity. We fix the item embedding's size to 50. For GRU4Rec<cit.>, a two layer GRU networks with hidden size 50 is used. For NextItNet<cit.>, we set the channel number to 50, kernel size to 3, and utilize the 8 layers convolutions with the dilation configuration of {1,2,4,8,1,2,4,8} proposed in the paper. For SASRec<cit.>, we use two layer transformer networks <cit.> with hidden size of 50. Following <cit.>, we set the dropout rate to 0.2 for MovieLens-1M while 0.5 for other sparser datasets. We tune the hyperparameters using the validation set and terminate training if its performance does not improve for 40 epochs.

For each backbone sequential network, we test the following methods and run 10 times to report the results. For DCR and its variants, we use the architecture depicted at Figure <ref>(b). For other methods, we use the architecture of Figure <ref>(a) and take $pref_{u,t}$ from Eq (<ref>) as the user embedding for further processing.

* $Base_{bce}$: This is the base method with BCE<cit.> loss.

* $Base_{bpr}$: This is the base method with BPR<cit.> loss.

* $BiasTower$<cit.>: With BCE loss, it learns a bias score with the item tower, which is added to the prediction during training while removed during inference.

* $IPW_{bce}$<cit.>: A SOTA IPW debiasing method with BCE loss.

* $IPW_{bpr}$<cit.>: A SOTA IPW debiasing method with BPR loss.

* $DICE$<cit.>: A structural causal model method that disentangles conformity and interest embeddings for superior performance.

* $PD$<cit.>: A structural causal model method that performs deconfounded training with do-calculus. As we care unbiased prediction rather than popularity drift here, PD is tested here instead of PDA.

* $MACR$<cit.>: A structural causal model method conducting counterfactual reasoning for unbiased prediction.

* $DCR_{var0}$: $w^{int}$ in Equation (<ref>) is fixed to 1.0. This actually masks the match between user conformity and item popularity embeddings to show the impact.

* $DCR_{var1}$: We do not use Equation (<ref>) and directly add $\hat{y}^{con}_m$ and $\hat{y}^{int}_m$ to compute $\hat{y}_m$. And $\alpha$ in Equation (<ref>) is set to 0.0 to totally disable $L^{int}_m$ and $L^{con}_m$.

* $DCR_{var2}$: We do not use Equation (<ref>) and directly add $\hat{y}^{con}_m$ and $\hat{y}^{int}_m$ to compute $\hat{y}_m$.

* $DCR$: The proposed approach.

For DCR, we tune $\alpha$ and $\beta$ from {2e-4,2e-3,2e-2,2e-1,2e0}. DCR's $\gamma$ is tuned from 5e-4 to 50. As for $c$ of DCR and MACR, it is tuned from 0 to 80 with a step of 10. Following <cit.>, for PD, we tune its hyperparameter of $\gamma$ from 0.02 to 0.25. For DICE, we also carefully tune its curriculum learning hyper-parameters following <cit.>.

§.§.§ Evaluation Metrics

We adopt two common metrics for recommendation, namely NDCG@10 and Hit Rate@10 <cit.>. Hit Rate counts the fraction of times that the ground-truth
next item is among the top K items, while NDCG assigns heavier weight for front positions. To alleviate the heavy computation, following <cit.>, we randomly sample 100 negative items for each ground-truth item and rank these items together to report the result. Moreover, for unbiased evaluation, we re-weight each testing instance with Inverse Propensity Weighting of the positive item to compute the mean value of NDCG and Hit Rate. We also calculate NDCG and Hit Rate with K=5 and 20 and find pretty consistent trend, which are omitted for space constraint.

§.§ Overall Offline Performance(RQ1)

Please refer to table <ref> for the overall results. Except for the hit rate metric of SASRec and GRU4Rec model on the Video Games dataset, DCR achieves significant relative gains over the best competitors. Among the competitors, $MACR$, $DICE$, $PD$ and $IPW_{bpr}$ performs the best for different scenarios. Comparing $IPW_{bpr}$ and $IPW_{bce}$, we can see $IPW_{bpr}$ generates better results. And $IPW_{bce}$ is sometimes worse than $Base_{bce}$, which also reflects the large variance issue of IPW methods. Comparing $MACR$ and $BiasTower$, we can see $BiasTower$ performs relatively stable across different datasets while $MACR$'s results fluctuate. We further investigate why $MACR$ performs bad on video games dataset and find its $L_i$ in equation (<ref>) is much worse than $DCR$ in those scenarios. We think the reason is that $MACR$'s representations are affected more by the potential negative transfer among multiple losses without embedding disentanglement. $DICE$ performs well for Steam while not so satisfactory for the other two datasets, for which we cannot tune well for the curriculum learning hyper-parameters of $DICE$.

§.§ Further Analysis for Offline Experiments(RQ2&RQ3)

§.§.§ Ablation Studies

Please refer to table <ref>, the three DCR variants perform worse than DCR, which proves the necessity of introducing the corresponding components. Comparing with others, $DCR_{var0}$ performs worst indicating the necessity to consider the match between user conformity and item popularity. Furthermore, $DCR_{var1}$ performs worse than $DCR_{var2}$, which verifies $L^{int}_{m}$ at equation (<ref>) and $L^{con}_{m}$ at (<ref>) can help to boost the recommendation performance. Finally, comparing $DCR_{var2}$ and $DCR$, we are sure the attention score introduced in Equation (<ref>) for conformity match and interest match is essential for superior results.

§.§.§ Sensitivity Tests

For the hyperparameters of multi-task loss weights, please refer to table <ref> for the model performance with varying $\alpha$ and $\beta$ and refer to table <ref> for tuning $\gamma$. Firstly, tuning these parameters will impact the recommendation quality, which also shows the effectiveness of the corresponding components. Moreover, the performance change gradually and predictably. And the best results for different datasets and models can be obtained with the same configuration. Among the three, the model is most sensitive to the change of $\beta$. For $\gamma$, initially, the model will perform better with the effective improvement of orthogonality loss from Equation (<ref>). But once the orthogonality loss is near 1e-3, further increasing $\gamma$ will produce worse results since the main loss can be negatively impacted with a too large $\gamma$.

Please refer to table <ref> for the impact of $c$. We can see tuning $c$ well is important for the model performance. And for different datasets, the best $c$ can vary greatly but the performance trend is predictable. Overall, DCR can be practically tuned and applied well.

[$Base_{bce}$'s NDCG over Emb Dim]

[$DCR$ vs $Base_{bce}$]

SASRec Results with Explicit User Embeddings

§.§.§ The Effect of Explicit User Embedding

We also test SASRec's performance of adding explicit user embeddings with different dimensions, disentangling them to conformity and interest representations and leveraging the two layer MLP structure (50,50) to merge with the sequential embeddings. We see relatively marginal gains for MovieLens-1M and Video Games while much worse results for Steam. For Steam, there are so many users so adding user embedding may cause the overfitting issue. We plot $Base_{bce}$'s NDCG@10 results of MovieLens-1M and Steam at Figure <ref>.  Moreover, excluding size 0, MovieLens-1M and Steam's best dimension size are 25 and 6 respectively. Based on such settings, DCR still achieves significant NDCG gains according to Figure <ref>.

The effect of tuning $\gamma$ on the backbone of SASRec

Dataset                                 $\gamma$     NDCG@10     Hit Rate@10     Orthogonality

1M           0            0.4317      0.6451          0.155

5e-4         0.4363      0.6579          0.099

5e-3         0.4387      0.6495          0.061

5e-2         0.4378      0.6669          0.016

5e-1         0.4438      0.6680          0.002

5e0          0.4216      0.6427          0.001

5e1          0.4011      0.6322          0.001

Video Games     0            0.3091      0.4982          0.153

5e-4         0.3128      0.4962          0.051

5e-3         0.3136      0.4993          0.027

5e-2         0.3135      0.5052          0.014

5e-1         0.3217      0.5059          0.001

5e0          0.3118      0.4931          0.0005

5e1          0.2955      0.4818          0.0001

The effect of tuning $c$ on the backbone of SASRec

Dataset                                 $c$     NDCG@10             Hit Rate@10

9*MovieLens-1M           0       0.3866              0.5902

10      0.4223              0.6361

20      0.4306              0.6480

30      0.4438     0.6680

40      0.4273              0.6604

50      0.4239              0.6615

60      0.3903              0.6183

70      0.3759              0.6096

80      0.3662              0.5952

9*Amazon Video Games     0       0.2903              0.4699

10      0.2909              0.4741

20      0.2979              0.4803

30      0.3029              0.4870

40      0.3006              0.4855

50      0.3132              0.4958

60      0.3159              0.5020

70      0.3217     0.5059

80      0.3096              0.4979

§.§ Online A/B Testing(RQ4)

We applied the proposed method to the ranking stage <cit.> of an industrial large-scale video recommender system. As the sequential feature of previous user actions is the most important feature for the ranking model based on our analysis, it is a typical sequential recommender system. In contrast to the aforementioned public data listed in table <ref>, other user features, such as user id, also generate considerable gains for the industrial data. The main online metrics include effective VV(count of Video Views with watch time exceeding a threshold) and total video watch time. Initially, we applied MACR<cit.> and saw a significant (p<0.05) increase of 0.79% for effective VV while neutral result for video watch time. Then, we replaced MACR with DCR and observed significant (p<0.05) additional gain of 0.85% and 1.53% for effective VV and video watch time respectively. Furthermore, we classified the items as long-tail (<5,000 impressions), hot (>180,000 impressions), and others based on their exposure count before the A/B testing. We observed 4.69% more exposure for long-tail items, less exposure of 1.01% for hot items, and marginal change for the other items by substituting DCR for MACR.

§ CONCLUSION

In this paper, we propose a structural causal model-based method, named DCR, to counteract the popularity bias with disentangled counterfactual reasoning in the sequential recommendation and achieve superior recommendation performance. We conduct thorough offline experiments to prove DCR significantly enhances the recommendation relevance over the existing debiasing methods on multiple real-world datasets and across various sequential base models. Moreover, extensive ablation studies are carried out to verify and understand the effectiveness of each newly introduced component. Finally, DCR achieves significant online gains over the SOTA debiasing methods based on the A/B tests.

#1     #1#1#1     #1     #1     #1     #1#1          #1#1

[Abdollahpouri et al(2019)]
authorpersonHiman Abdollahpouri, personRobin
Burke, and personBamshad Mobasher.
Managing popularity bias in recommender systems
with personalized re-ranking. In booktitleThe thirty-second
international flairs conference.

[Bonner and Vasile(2018)]
authorpersonStephen Bonner and
personFlavian Vasile. year2018.
Causal embeddings for recommendation. In
booktitleProceedings of the 12th ACM conference on
recommender systems. pages104–112.

[Bottou et al(2013)]
authorpersonLéon Bottou, personJonas
Peters, personJoaquin Quiñonero-Candela,
personDenis X Charles, personD Max Chickering,
personElon Portugaly, personDipankar Ray,
personPatrice Simard, and personEd Snelson.
Counterfactual Reasoning and Learning Systems: The
Example of Computational Advertising.
journalJournal of Machine Learning Research
volume14, number11 (year2013).

[Burges et al(2005)]
authorpersonChris Burges, personTal Shaked,
personErin Renshaw, personAri Lazier,
personMatt Deeds, personNicole Hamilton, and
personGreg Hullender. year2005.
Learning to rank using gradient descent. In
booktitleProceedings of the 22nd international conference on
Machine learning. pages89–96.

[Chen et al(2020)]
authorpersonJiawei Chen, personHande Dong,
personXiang Wang, personFuli Feng,
personMeng Wang, and personXiangnan He.
Bias and debias in recommender system: A survey and
future directions.
journalarXiv preprint arXiv:2010.03240

[Chen et al(2022)]
authorpersonZhihong Chen, personJiawei Wu,
personChenliang Li, personJingxu Chen,
personRong Xiao, and personBinqiang Zhao.
Co-training Disentangled Domain Adaptation Network
for Leveraging Popularity Bias in Recommenders. In
booktitleProceedings of the 45th International ACM SIGIR
Conference on Research and Development in Information Retrieval.

[Cho et al(2014)]
authorpersonKyunghyun Cho, personBart
Van Merriënboer, personCaglar Gulcehre,
personDzmitry Bahdanau, personFethi Bougares,
personHolger Schwenk, and personYoshua Bengio.
Learning phrase representations using RNN
encoder-decoder for statistical machine translation.
journalarXiv preprint arXiv:1406.1078

[Covington et al(2016)]
authorpersonPaul Covington, personJay
Adams, and personEmre Sargin.
Deep neural networks for youtube recommendations.
In booktitleProceedings of the 10th ACM conference on
recommender systems. pages191–198.

[Donkers et al(2017)]
authorpersonTim Donkers, personBenedikt
Loepp, and personJürgen Ziegler.
Sequential user-based recurrent neural network
recommendations. In booktitleProceedings of the eleventh
ACM conference on recommender systems. pages152–160.

authorpersonAlex Graves.
Long short-term memory.
journalSupervised sequence labelling with recurrent
neural networks (year2012), pages37–45.

[Gruson et al(2019)]
authorpersonAlois Gruson, personPraveen
Chandar, personChristophe Charbuillet, personJames
McInerney, personSamantha Hansen, personDamien
Tardieu, and personBen Carterette.
Offline evaluation to make decisions about
playlistrecommendation algorithms. In booktitleProceedings
of the Twelfth ACM International Conference on Web Search and Data Mining.

[Harper and Konstan(2015)]
authorpersonF Maxwell Harper and
personJoseph A Konstan. year2015.
The movielens datasets: History and context.
journalAcm transactions on interactive intelligent
systems (tiis) volume5, number4
(year2015), pages1–19.

[He et al(2017)]
authorpersonXiangnan He, personLizi Liao,
personHanwang Zhang, personLiqiang Nie,
personXia Hu, and personTat-Seng Chua.
Neural collaborative filtering. In
booktitleProceedings of the 26th international conference on
world wide web. pages173–182.

[He et al(2022)]
authorpersonXiangnan He, personYang Zhang,
personFuli Feng, personChonggang Song,
personLingling Yi, personGuohui Ling, and
personYongdong Zhang. year2022.
Addressing Confounding Feature Issue for Causal
journalarXiv preprint arXiv:2205.06532

[Hidasi and Karatzoglou(2018)]
authorpersonBalázs Hidasi and
personAlexandros Karatzoglou. year2018.
Recurrent neural networks with top-k gains for
session-based recommendations. In booktitleProceedings of
the 27th ACM international conference on information and knowledge
management. pages843–852.

[Hidasi et al(2015)]
authorpersonBalázs Hidasi,
personAlexandros Karatzoglou, personLinas Baltrunas,
and personDomonkos Tikk. year2015.
Session-based recommendations with recurrent neural
journalarXiv preprint arXiv:1511.06939

[Kang and McAuley(2018)]
authorpersonWang-Cheng Kang and
personJulian McAuley. year2018.
Self-attentive sequential recommendation. In
booktitle2018 IEEE international conference on data mining
(ICDM). IEEE, pages197–206.

[Kenton and Toutanova(2019)]
authorpersonJacob Devlin Ming-Wei Chang Kenton and
personLee Kristina Toutanova. year2019.
Bert: Pre-training of deep bidirectional
transformers for language understanding. In
booktitleProceedings of naacL-HLT.

[Koren et al(2009)]
authorpersonYehuda Koren, personRobert
Bell, and personChris Volinsky.
Matrix factorization techniques for recommender
journalComputer volume42,
number8 (year2009), pages30–37.

[Lee et al(2021)]
authorpersonJae-woong Lee, personSeongmin
Park, and personJongwuk Lee. year2021.
Dual Unbiased Recommender Learning for Implicit
Feedback. In booktitleProceedings of the 44th International
ACM SIGIR Conference on Research and Development in Information Retrieval.

[Li et al(2017)]
authorpersonJing Li, personPengjie Ren,
personZhumin Chen, personZhaochun Ren,
personTao Lian, and personJun Ma.
Neural attentive session-based recommendation. In
booktitleProceedings of the 2017 ACM on Conference on
Information and Knowledge Management. pages1419–1428.

[McAuley et al(2015)]
authorpersonJulian McAuley,
personChristopher Targett, personQinfeng Shi, and
personAnton Van Den Hengel. year2015.
Image-based recommendations on styles and
substitutes. In booktitleProceedings of the 38th
international ACM SIGIR conference on research and development in information
retrieval. pages43–52.

authorpersonLeland Gerson Neuberg.
Causality: models, reasoning, and inference, by
judea pearl, cambridge university press, 2000.
journalEconometric Theory volume19,
number4 (year2003), pages675–685.

[Qin et al(2020)]
authorpersonZhen Qin, personSuming J Chen,
personDonald Metzler, personYongwoo Noh,
personJingzheng Qin, and personXuanhui Wang.
Attribute-based propensity for unbiased learning in
recommender systems: Algorithm and case studies. In
booktitleProceedings of the 26th ACM SIGKDD International
Conference on Knowledge Discovery & Data Mining.

[Rendle et al(2012)]
authorpersonSteffen Rendle, personChristoph
Freudenthaler, personZeno Gantner, and personLars
Schmidt-Thieme. year2012.
BPR: Bayesian personalized ranking from implicit
journalarXiv preprint arXiv:1205.2618

[Rendle et al(2010)]
authorpersonSteffen Rendle, personChristoph
Freudenthaler, and personLars Schmidt-Thieme.
Factorizing personalized markov chains for
next-basket recommendation. In booktitleProceedings of the
19th international conference on World wide web. pages811–820.

[Rosenbaum and Rubin(1983)]
authorpersonPaul R Rosenbaum and
personDonald B Rubin. year1983.
The central role of the propensity score in
observational studies for causal effects.
journalBiometrika volume70,
number1 (year1983), pages41–55.

authorpersonYuta Saito.
Unbiased Pairwise Learning from Biased Implicit
Feedback. In booktitleProceedings of the 2020 ACM SIGIR on
International Conference on Theory of Information Retrieval.

[Saito et al(2020)]
authorpersonYuta Saito, personSuguru
Yaginuma, personYuta Nishino, personHayato Sakata,
and personKazuhide Nakata. year2020.
Unbiased recommender learning from
missing-not-at-random implicit feedback. In
booktitleProceedings of the 13th International Conference on
Web Search and Data Mining. pages501–509.

[Schnabel et al(2016)]
authorpersonTobias Schnabel, personAdith
Swaminathan, personAshudeep Singh, personNavin
Chandak, and personThorsten Joachims.
Recommendations as treatments: Debiasing learning
and evaluation. In booktitleinternational conference on
machine learning. PMLR, pages1670–1679.

[Shani et al(2005)]
authorpersonGuy Shani, personDavid
Heckerman, personRonen I Brafman, and personCraig
Boutilier. year2005.
An MDP-based recommender system.
journalJournal of Machine Learning Research
volume6, number9 (year2005).

[Sun et al(2019)]
authorpersonFei Sun, personJun Liu,
personJian Wu, personChanghua Pei,
personXiao Lin, personWenwu Ou, and
personPeng Jiang. year2019.
BERT4Rec: Sequential recommendation with
bidirectional encoder representations from transformer. In
booktitleProceedings of the 28th ACM international
conference on information and knowledge management.

[Swaminathan and Joachims(2015)]
authorpersonAdith Swaminathan and
personThorsten Joachims. year2015.
The self-normalized estimator for counterfactual
journaladvances in neural information processing
systems  volume28 (year2015).

[Tang and Wang(2018)]
authorpersonJiaxi Tang and personKe
Wang. year2018.
Personalized top-n sequential recommendation via
convolutional sequence embedding. In booktitleProceedings
of the eleventh ACM international conference on web search and data mining.

[Tuan and Phuong(2017)]
authorpersonTrinh Xuan Tuan and
personTu Minh Phuong. year2017.
3D convolutional networks for session-based
recommendation with content features. In
booktitleProceedings of the eleventh ACM conference on
recommender systems. pages138–146.

[Vaswani et al(2017)]
authorpersonAshish Vaswani, personNoam
Shazeer, personNiki Parmar, personJakob Uszkoreit,
personLlion Jones, personAidan N Gomez,
personŁukasz Kaiser, and personIllia
Polosukhin. year2017.
Attention is all you need.
journalAdvances in neural information processing
systems  volume30 (year2017).

[Wang et al(2021)]
authorpersonWenjie Wang, personFuli Feng,
personXiangnan He, personXiang Wang, and
personTat-Seng Chua. year2021.
Deconfounded recommendation for alleviating bias
amplification. In booktitleProceedings of the 27th ACM
SIGKDD Conference on Knowledge Discovery & Data Mining.

[Wang et al(2022)]
authorpersonZhenlei Wang, personShiqi Shen,
personZhipeng Wang, personBo Chen,
personXu Chen, and personJi-Rong Wen.
Unbiased Sequential Recommendation with Latent
Confounders. In booktitleProceedings of the ACM Web
Conference 2022. pages2195–2204.

[Wei et al(2021)]
authorpersonTianxin Wei, personFuli Feng,
personJiawei Chen, personZiwei Wu,
personJinfeng Yi, and personXiangnan He.
Model-agnostic counterfactual reasoning for
eliminating popularity bias in recommender system. In
booktitleProceedings of the 27th ACM SIGKDD Conference on
Knowledge Discovery & Data Mining. pages1791–1800.

[Wikipedia contributors(2022)]
authorpersonWikipedia contributors.
titleGini coefficient — Wikipedia, The Free
howpublished<https://en.wikipedia.org/w/index.php?title=Gini_coefficient   oldid=1101796604>.
[Online; accessed 9-August-2022].

[Xue et al(2017)]
authorpersonHong-Jian Xue, personXinyu Dai,
personJianbing Zhang, personShujian Huang, and
personJiajun Chen. year2017.
Deep matrix factorization models for recommender
systems.. In booktitleIJCAI, Vol. volume17.
Melbourne, Australia, pages3203–3209.

[Yu et al(2016)]
authorpersonFeng Yu, personQiang Liu,
personShu Wu, personLiang Wang, and
personTieniu Tan. year2016.
A dynamic recurrent model for next basket
recommendation. In booktitleProceedings of the 39th
International ACM SIGIR conference on Research and Development in Information
Retrieval. pages729–732.

[Yuan et al(2019)]
authorpersonFajie Yuan, personAlexandros
Karatzoglou, personIoannis Arapakis, personJoemon M
Jose, and personXiangnan He. year2019.
A simple convolutional generative network for next
item recommendation. In booktitleProceedings of the twelfth
ACM international conference on web search and data mining.

[Zhang et al(2021)]
authorpersonYang Zhang, personFuli Feng,
personXiangnan He, personTianxin Wei,
personChonggang Song, personGuohui Ling, and
personYongdong Zhang. year2021.
Causal intervention for leveraging popularity bias
in recommendation. In booktitleProceedings of the 44th
International ACM SIGIR Conference on Research and Development in Information
Retrieval. pages11–20.

[Zheng et al(2021)]
authorpersonYu Zheng, personChen Gao,
personXiang Li, personXiangnan He,
personYong Li, and personDepeng Jin.
Disentangling user interest and conformity for
recommendation with causal embedding. In
booktitleProceedings of the Web Conference 2021.

[Zhu et al(2020)]
authorpersonZiwei Zhu, personYun He,
personYin Zhang, and personJames Caverlee.
Unbiased implicit recommendation and propensity
estimation via combinational joint learning. In
booktitleFourteenth ACM Conference on Recommender Systems.

[Zhu et al(2021)]
authorpersonZiwei Zhu, personYun He,
personXing Zhao, personYin Zhang,
personJianling Wang, and personJames Caverlee.
Popularity-opportunity bias in collaborative
filtering. In booktitleProceedings of the 14th ACM
International Conference on Web Search and Data Mining.