Trustworthiness-aware knowledge graph representation for recommendation

Incorporating knowledge graphs (KGs) into recommender systems (RS) has recently attracted increasing attention. For large-scale KGs, due to limited labour supervision, noises are inevitably introduced during automatic construction. However, the effects of such noises as untrustworthy information in KGs on RS are unclear, and how to retain RS performing well while encountering such untrustworthy information has yet to be solved. Motivated by them, we study the effects of the trustworthiness of the KG on RS and propose a novel method trustworthiness-aware knowledge graph representation (KGR) for recommendation (TrustRec). TrustRec introduces a trustworthiness estimator into noise-tolerant KGR methods for collaborative filtering. Specifically, to assign trustworthiness, we leverage internal structures of KGs from microscopic to macroscopic levels: motifs, communities and global information, to reflect the true degree of triple expression. Building on this estimator, we then propose trustworthiness integration to learn noise-tolerant KGR and item representations for RS. We conduct extensive experiments to show the superior performance of TrustRec over state-of-the-art recommendation methods.