Top-N Recommendation with Counterfactual User Preference Simulation

Yang, Mengyue; Dai, Quanyu; Dong, Zhenhua; Xu, Chen; He, Xiuqiang; Wang, Jun

doi:10.1145/3459637.3482305

Cited by 44 publications

(20 citation statements)

References 43 publications

(60 reference statements)

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“…Recently, causal inference (CI) has drawn a lot of attention in neural language processing (NLP) [10], computer vision (CV) [36,57] and recommender system (RS) [4,30,47,60,66,69]. Instead of exploiting the correlation relationships between input and output by feeding data to the black-box neural networks, CI explicitly models the causal mechanism among variables [18,43].…”

Section: Causality-enhanced Recommendationmentioning

confidence: 99%

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

Gao¹,

Lei²,

Chen³

et al. 2022

Preprint

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While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less satisfied. Existing work studies filter bubbles in static recommendation, where the effect of overexposure is hard to capture. In contrast, we believe it is more meaningful to study the issue in interactive recommendation and optimize long-term user satisfaction. Nevertheless, it is unrealistic to train the model online due to the high cost. As such, we have to leverage offline training data and disentangle the causal effect on user satisfaction.To achieve this goal, we propose a counterfactual interactive recommender system (CIRS) that augments offline reinforcement learning (offline RL) with causal inference. The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction. It then uses the learned causal user model to help the planning of the RL policy. To conduct evaluation offline, we innovatively create an authentic RL environment (KuaiEnv) based on a real-world fully observed user rating dataset. The experiments show the effectiveness of CIRS in bursting filter bubbles and achieving long-term success in interactive recommendation. The implementation of CIRS is available via https://github.com/chongminggao/CIRS-codes.

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Section: Causality-enhanced Recommendationmentioning

confidence: 99%

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

Gao¹,

Lei²,

Chen³

et al. 2022

Preprint

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“…Multi-task learning with task dependency is a challenging subject in recommender system because dependencies between tasks make feedback labels MNAR [17,24]. Current works in this community can be broadly divided into two groups with respect to the methodology used to address the MNAR problem.…”

Section: Related Workmentioning

confidence: 99%

ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation

Wang,

Chang,

Liu

et al. 2022

Preprint

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Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, i.e., 𝑖𝑚𝑝𝑟𝑒𝑠𝑠𝑖𝑜𝑛 → 𝑐𝑙𝑖𝑐𝑘 → 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems:(1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM 2 ), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM 2 can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.

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“…Some works, including [42] proposed to leverage the good aspects of popularity bias and deconfound the bad aspects for improving recommendations. Moreover, to improve the personalized rankings of recommender systems [39] proposed to apply Pearl's causal inference framework [27]. Compared to earlier causal recommendation works, our work differs in two ways.…”

Section: Related Workmentioning

confidence: 99%

Causal Disentanglement with Network Information for Debiased Recommendations

Sheth¹,

Guo²,

Liu³

et al. 2022

Preprint

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Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of information about multiple factors, including user's interests, item attributes along with confounding factors such as user conformity, and item popularity. Considering these entangled representations for inferring user preference may lead to biased recommendations (e.g., when the recommender model recommends popular items even if they do not align with the user's interests). Recent research proposes to debias by modeling a recommender system from a causal perspective. The exposure and the ratings are analogous to the treatment and the outcome in the causal inference framework, respectively. The critical challenge in this setting is accounting for the hidden confounders. These confounders are unobserved, making it hard to measure them.On the other hand, since these confounders affect both the exposure and the ratings, it is essential to account for them in generating debiased recommendations. To better approximate hidden confounders, we propose to leverage network information (i.e., user-social and user-item networks), which are shown to influence how users discover and interact with an item. Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of causal disentanglement which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders. Experiments on real-world datasets validate the effectiveness of the proposed model for debiasing recommender systems.

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Top-N Recommendation with Counterfactual User Preference Simulation

Cited by 44 publications

References 43 publications

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System

ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation

Causal Disentanglement with Network Information for Debiased Recommendations

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