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Graph collaborative filtering (GCF) has emerged as a prominent method in recommendation systems, leveraging the power of graph learning to enhance traditional collaborative filtering (CF). One common approach in GCF involves employing Graph ...

Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems. Existing fairness-aware approaches ignore accounting for rich user and item attributes and thus cannot capture the impact of attributes on ...

Multi-behavior recommendations (MBRs) aim to enhance recommendation performance with multi-typed user-item interactions. This paper approaches MBR from a causal perspective, treating the predictions of MBR as outcomes, given various user ...${}^{\mathrm{}}$

Counterfactual explanations interpret the recommendation mechanism by exploring how minimal alterations on items or users affect recommendation decisions. Existing counterfactual explainable approaches face huge search space, and their explanations are ...

Conversational Recommender Systems (CRSs) fundamentally differ from traditional recommender systems by interacting with users in a conversational session to accurately predict their current preferences and provide personalized recommendations. Although ...

Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation ...

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users’ true intent and thus deteriorate the recommendation effectiveness. ...

Traditional recommenders suffer from hidden confounding factors, leading to the spurious correlations between user/item profiles and user preference prediction, i.e., the confounding bias issue. Most works resort to only one confounding bias, ...

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called ...

Off-policy learning has drawn huge attention in recommender systems (RS), which provides an opportunity for reinforcement learning to abandon the expensive online training. However, off-policy learning from logged data suffers biases caused by the policy ...

Although traditional recommendation methods trained on observational interaction information have engendered a significant impact in real-world applications, it is challenging to disentangle users’ true interests from interaction data. Recent ...

Reinforcement learning has recently become an active topic in recommender system research, where the logged data that records interactions between items and users feedback is used to discover the policy. Much off-policy learning, referring to the ...

Film industries all over the world are producing several hundred movies rapidly and grabbing the attraction of people of all ages. Every movie producer is of keen interest in knowing which movies are either likely to hit or flop in the box office. ...

Sequential recommendation leverages the temporal information of users’ transactions as transition dependencies for better inferring user preference, which has become increasingly popular in academic research and practical applications. Short-term ...