Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences 论文

详细信息

发表日期

2019-05-13

发表年份

2019

摘要

Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the ”knowledge” in KG at the shallow level of entity raw data or embeddings. This may lead to suboptimal performance, since a practical KG can hardly be complete, and it is common that a KG has missing facts, relations, and entities. Thus, we argue that it is crucial to consider the incomplete nature of KG when incorporating it into recommender system.