Multi-criteria (MC) ratings can improve the predictive performance of recommender systems by introducing fine-grained auxiliary information. The existing MC methods usually model each criterion rating independently and leverage a utility function (e.g., simple averaging) for recommendation purpose. However, this modeling paradigm ignores two important aspects. First, the MC rating view significantly increases data sparsity, which poses a serious challenge for mining users’ MC preferences. Second, the noisy interactions carried by MC ratings may generate misleading decision results. To jointly address the above limitations, we propose a Self-supervised Multi-Criteria Recommendation (SMCR) framework, which aims to mitigate the negative impact caused by the data sparsity and noise problems. Specifically, the SMCR first models higher-order dependencies among users and items based on graph convolutional neural networks. Subsequently, an inter-criterion attentional mechanism is constructed to measure the heterogeneity exhibited by users in their MC preferences. Moreover, we design a cross-criteria self-supervised learning task that augments the robustness of embedding representations through knowledge transfer among views. The optimization objective of this task effectively suppresses the interference of sparse environment and noise on the model. The experimental results in four real scenarios demonstrate that the proposed SMCR outperforms the state-of-the-art MC baselines.

Explainable recommendation aims to offer high-quality explanations for recommendation results to help users in understanding and making decisions. Benefiting from the intuitive and easy-to-understand nature of natural language, existing work primarily concentrates on text-based explainable recommendation methods with the objective of generating explanatory text. However, most of these methods only focus on textual information while neglecting the multimodal information that can provide more comprehensive preferences and characteristics. To address this issue, we propose a novel Multimodal Enhanced Explainable Recommendation (MEER) model. Specifically, we integrate collaborative features and multimodal features into a multitask learning framework, jointly optimizing the tasks of rating prediction and explanation generation. Firstly, the pre-trained model CLIP is utilized to extract multimodal features. Subsequently, we adopt an attention mechanism to fuse the collaborative and multimodal features. To fully model the cross-modal feature correlations, we introduce a contrastive learning paradigm to constrain the fused features. Furthermore, we developed a personalized multimodal Transformer decoder, which is capable of comprehensively modeling both collaborative information and multimodal information. Experimental results on three real-world datasets demonstrate that our proposed method has achieved competitive performance in both recommendation accuracy and explanation quality.

Multi-criteria (MC) ratings as auxiliary supervisory signals can improve the prediction accuracy of recommender systems. The existing MC methods learn the representations of users and items in Euclidean space to estimate the interaction probabilities. However, this modeling paradigm ignores two important aspects. Firstly, when embedding power-law distribution data and personalized MC preferences in Euclidean space, the model may produce suboptimal solutions due to the distortion of the hierarchical structure. Secondly, the inevitable noise in MC ratings may hinder the recommendation quality of the model. To address the above issues, we propose a novel framework called Hyperbolic Multi-Criteria Recommendation (HMCR), which aims to mine users' MC behavioral features on hyperbolic manifolds and mitigate the noise interference through knowledge transfer among the criteria. Specifically, we map the representations on each criterion view to a hyperbolic space with adjustable curvature based on the Lorentz model, which is used to capture the hierarchical structure of collective user behavior. The MC preferences of individual users are fused by calculating the hyperbolic attention among each criterion and the overall rating. Moreover, we design a self-supervised contrastive loss to suppress the negative impact of noise interactions on the model. The experimental results on four real-world datasets show that the HMCR significantly outperforms the existing baselines.

Recommender systems are designed to assist users in discovering interesting items and bringing profits to online platforms. The existing works primarily explore the correlation between historical feedback and model predictions through the data-driven paradigm based on a single user-item rating matrix (i.e., overall rating). However, this single-criterion methods ignore the users’ multi-criteria (MC) behavioral characteristics. For example, a hotel system allows users to rate from multiple dimensions, such as environment and location (i.e., MC ratings). Moreover, selection bias is pervasive in user behavior data. Traditional data-driven methods may induce spurious association and amplified biases. To address the above challenges, we propose a debiasing framework called Multi-Criteria Causal Recommendation (MCCR), which encapsulates users’ diverse MC preferences and employs causal inference to construct novel training and inference strategies. Specifically, we first represent the causal relationships among variables in MC scenarios through the structural causal model. Then, we mitigate the negative impact of selection bias through the back-door adjustment. Next, a graph representation learning framework suitable for MC ratings is developed, which is used to extract higher-order information and infer the heterogeneity of users’ preferences with different criteria. Experimental results on six real datasets demonstrate that the MCCR significantly outperforms the existing baselines.