Assessing Sentiment Semantics in KG-Based Explainable Recommender Systems

Abstract

Sentiment information can enhance path-based explainable recommendation, yet advances are hindered by the lack of public benchmarks for sentiment-aware knowledge graph (SAKG) reasoning. We address this gap by systematically evaluating how different sentiment representation strategies and reward mechanisms affect utility, beyond-utility, and explanation quality across four real-world datasets under our open-source SAKG reasoning benchmark. Our exploration reveals: (1) sentiment-aware methods improve accuracy and diversity but reduce provider fairness, exposing a consistent trade-off; (2) under extreme sentiment imbalance, adaptive entropy-weighted meta-relations improve robustness in some domains, while naive sentiment sharing across heterogeneous relations causes negative transfer with considerable utility drop; (3) sentiment path consistency rewards enhance recommendation quality and explanation coherence, whereas terminal-only sentiment constraints improve fairness at modest utility cost. Our findings and open benchmark provide actionable guidance for designing sentiment-aware explainable recommenders and a foundation for future research in this underexplored space.