A review of recommender systems based on knowledge graph embedding

With the exponential growth of data volume, information overload has become a problem for users, and recommender systems were employed to solve this problem. The purpose of the recommender system is to mine the information of interest to users from massive amounts of data. However, some problems have not been well addressed in recommender systems, e.g., the sparse interaction data between users and items and the cold-start problems when making recommendations to new users. In recent years, incorporating knowledge graphs as side information to recommender systems by knowledge graph embedding techniques has attracted considerable interest, because the rich information contained in the knowledge graph can effectively solve the above problems. This paper provides a systematic review of recommender systems based on knowledge graph embedding in terms of methods and applications. Specifically, some basic notions of recommender systems and knowledge graphs are first briefly introduced, followed by a detailed description of how existing methods associate knowledge graph embedding and recommender systems. In addition, a series of related recommendation application scenarios are summarized along with information and statistics on related datasets.