Incorporating knowledge graphs into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. We have developed a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graphs for recommendation.
Dingxian Wang is an applied researcher at eBay. He has been working with the eBay search science and marketing science for over 5 years. He published around 10 papers and patents on top conferences and journals in the field of information retrieval, data mining and artifical intelligence.
