Causal Inference for Multi-Criteria Rating Recommender Systems

Causal Inference for Multi-Criteria Rating Recommender Systems

연구 분야: Verification

학회: ACM Transactions on Information Systems, Volume 43, Issue 6

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.