Review-based recommendation methods typically integrate multiple behaviors, including interactions, reviews, and ratings, to model user preferences. To effectively extract preference signals from diverse behaviors, some studies train multiple student models to capture distinct behavioral patterns, and leverage online distillation to facilitate collaborative learning among them. However, we argue that these techniques suffer from bias contamination from rating distributions and feature homogenization during cross-behavior knowledge transfer: (1) Rating distribution bias, arising from non-uniform historical ratings, propagates across behaviors through distillation, contaminating the true preference representations of other behaviors. (2) Static distillation strategies often lead to homogenized behavioral features, hindering the learning of behavior-specific preferences. To address these issues, we propose a novel Bidirectional Counterfactual Distillation (BiCoD) framework for review-based recommendation. In BiCoD, we first design an adversarial counterfactual distillation module to suppress the impact of non-uniform rating distributions on distillation, thereby preventing it from contaminating the user's true preference representations across behaviors. Subsequently, we introduce a stage-aware bidirectional distillation strategy to enhance the distinctiveness of behavioral features, facilitating the effective learning of behavior-specific preferences. Extensive experiments on five real-world datasets validate the effectiveness and superiority of the proposed framework.

Rating prediction is a classic task in recommendation systems, aiming to accurately estimate user ratings for various items. Historical ratings typically exhibit a non-uniform distribution, leading recommendation models to favor predicting high-frequency ratings. We refer to the inconsistency between predicted ratings and users' true preferences caused by non-uniform rating distributions asrating bias. To mitigate this bias, existing studies capture various interaction behavior patterns and employ knowledge distillation techniques to improve the network's ability to model user preferences. However, due to the model being trained on datasets with non-uniform rating distributions, the rating bias may propagate through the knowledge distillation process across different behaviors, thereby contaminating the modeling of users' true preferences. To this end, we propose a novelAdversarial Counterfactual Distillation(ACD) framework for the rating prediction task, aimed at eliminating rating bias. Specifically, we design aCounterfactual Distillation Modulefrom a causal reasoning perspective to facilitate knowledge transfer across various interaction behaviors while concurrently mitigating bias contamination. Furthermore, we introduce anAdversarial Debiasing Moduleto dynamically adjust the debiasing strength, ensuring that the model maintains an optimal balance between effective knowledge transfer and bias mitigation. Extensive experiments demonstrate the superior performance of our proposed ACD framework. The complete code is publicly available athttps://github.com/hfutmars/ACD.

Rating prediction is a challenging task in review-aware recommendation. Although current methods effectively combine collaborative signals with review data, they fail to differentiate user preferences across various ratings and overlook the independence between these ratings. In this article, we emphasize the importance of independence modeling among representations for different rating levels. To this end, we propose a rating-oriented explicit disentangling graph convolution network for review-aware recommendation, short for REDGCN. Specifically, we introduce a rating-oriented disentangled representation learning that segments representations and rating graph based on ratings. It also employs an explicit graph learning approach to ensure the independence of disentangled representations during information propagation, which mitigates noise from review features. Furthermore, we define and model one kind of cross-rating correlation, based on the characteristics of user rating behavior. By leveraging this approach, we introduce a cross-rating constraint as an additional task to further enhance the independence among disentangled representations and improve the stability of model training. We conduct extensive experiments on six public datasets to prove the effectiveness of REDGCN. The complete data and codes of REDGCN are available athttps://github.com/hfutmars/REDGCN.

Multimedia-based recommendation (MMRec) is a challenging task, which goes beyond the collaborative filtering (CF) schema that only captures collaborative signals from interactions and explores multimodal user preference cues hidden in complex multimedia content. Despite the significant progress of current solutions for MMRec, we argue that they are limited by multimodal noise contamination. Specifically, a considerable amount of preference-irrelevant multimodal noise (e.g., the background, layout, and brightness in the image of the product) is incorporated into the representation learning of items, which contaminates the modeling of multimodal user preferences. Moreover, most of the latest researches are based on graph convolution networks (GCNs), which means that multimodal noise contamination is further amplified because noisy information is continuously propagated over the user–item interaction graph as recursive neighbor aggregations are performed. To address this problem, instead of the common MMRec paradigm which learns user preferences in an integrated manner, we propose a hierarchical framework to separately learn collaborative signals and multimodal preferences cues, thus preventing multimodal noise from flowing into collaborative signals. Then, to alleviate the noise contamination for multimodal user preference modeling, we propose to extract semantic entities from multimodal content that are more relevant to user interests, which can model semantic-level multimodal preferences and thus remove a large fraction of noise. Furthermore, we use the full multimodal features to model content-level multimodal preferences like the existing MMRec solutions, which ensures the sufficient utilization of multimodal information. Overall, we develop a novel model, multimodal hierarchical graph CF (MHGCF), which consists of three types of GCN modules tailored to capture collaborative signals, semantic-level preferences, and content-level preferences, respectively. We conduct extensive experiments to demonstrate the effectiveness of MHGCF and its components. The complete data and codes of MHGCF are available athttps://github.com/hfutmars/MHGCF.