Knowledge-Refined Causality-Inspired Preference Modeling for Recommender Systems

Recommender systems have become a key technology for intelligent information services by learning user preferences from large-scale behavioral data and heterogeneous knowledge sources. However, existing recommendation methods still face two critical limitations: noisy knowledge graphs may introduce misleading signals during representation learning, and it remains challenging to identify item attributes that are relevant to users’ observed preferences. To address these issues, we propose CaRecK (causality-inspired recommendation with knowledge graph), a knowledge-refined preference modeling framework that combines causality-inspired attribute relevance estimation with preference-aware knowledge aggregation. Specifically, we develop an attribute selection mechanism to identify preference-relevant attributes for user representation learning. Furthermore, a preference-aware strategy is designed to adaptively select informative neighbors and integrate semantic information for learning item representations. Extensive experiments on two real-world benchmark datasets demonstrate that CaRecK achieves competitive performance in both CTR prediction and Top-K recommendation tasks.