Personalized Dynamic Knowledge-Aware Recommendation with Hybrid Explanations

Abstract

Explainable recommendation is attracting more and more attention in both industry and research communities. While some existing models utilize reviews for improving the performance of recommender systems, most of them assume that user’s preference is static and each review’s importance is user-independent. However, it is intuitive that user’s preference is always dynamically changing and reviews from similar users should be given more importance as they share similar tastes. Moreover, they achieve model explainability at either feature level that is too concise or review level that is too redundant. To deal with these problems, we propose a Personalized Dynamic Knowledge-aware Recommender (PDKR) for dynamic user modeling and personalized item modeling. In particular, we model user’s preference with defined entities and relations in sequential knowledge graphs and capture its dynamics with a novel interval-aware Gated Recurrent Unit (GRU). Furthermore, by leveraging self-attention mechanism, we can not only learn each review’s user-specific importance, but also provide tailored explanations for each user at both feature level and review level. We conduct extensive experiments on three benchmark datasets from Amazon and Yelp and the results show that PDKR outperforms all the state-of-the-art recommendation approaches in rating prediction task while providing more effective explanations simultaneously.