GFE: General Knowledge Enhanced Framework for Explainable Sequential Recommendation

Abstract

It is vital for sequential recommendation to provide accurate and explainable results for user, which can help them make better decisions. In this paper, we develop a General Knowledge Enhanced Framework for Explainable Sequential Recommendation (GFE) to capture user’s fine-grained preferences and dynamic preferences evolution. Specifically, the fine-grained preferences are modeled as intrinsic interests and external potential interests, which can be captured by sequential-aware interest and knowledge-aware interest modules respectively. Moreover, the high-order paths between each user-item pair are generated with the help of the knowledge graph, which contain abundant high-order semantic relevance among entities. To make better use of this character, we propose a hierarchical self-attention mechanism to aggregate the high-order semantic information from these knowledge paths, thus discovering user’s dynamic preferences evolution. According to these abilities, GFE can provide more reasonable explanations from the views of microcosm and macrocosm. Unlike other traditional explainable sequential recommendation models, GFE has the strong generalization to integrate with other pure sequential recommendation models and endow explainability to them. Based on this nature, we combine the GFE with two state-of-the-art sequential recommendation models and further propose two GFE-based models, called GFE-SASRec and GFE-TiSASRec, to show the availability of GFE. Finally, experiments conducted on three real-world public datasets demonstrate the state-of-the-art performance and the strong explainability of GFE.