Knowledge-enhanced personalized hierarchical attention network for sequential recommendation

Abstract

Sequential recommendation aims to predict the next items that users will interact with according to the sequential dependencies within historical user interactions. Recently, self-attention based sequence modeling methods have become the mainstream method due to their competitive accuracy. Despite their effectiveness, these methods still have non-trivial limitations: (1) they mainly take the transition patterns between items into consideration but ignore the semantic associations between items, and (2) they mostly focus on dynamic short-term user preferences and fail to consider user static long-term preferences explicitly. To address these limitations, we propose a Knowledge Enhanced Personalized Hierarchical Attention Network (KPHAN), which can incorporate the semantic associations among items by learning from knowledge graphs and capture the fine-grained long- and short-term interests of users through a novel personalized hierarchical attention network. Specifically, we employ the entities and relationships in the knowledge graph to enrich semantic information for items while preserving the structural information of the knowledge graph. The self-attention mechanism then captures semantic associations among items to obtain short-term user preferences more accurately. Finally, a personalized hierarchical attention network is developed to generate the final user preference representations, which can fully capture user static long-term preferences while fusing dynamic short-term preferences. Experimental results on three real-world datasets demonstrate that our method can outperform prior works by 2.7% - 35.5% on HR metrics and 6.7% - 27.9% on NDCG metrics.