Abstract: Recommendation plays an important role in satisfying users needs of information and solving information overload. Heterogeneous information network contains rich semantics and provides a new way for recommendation optimization. Based on the research of recommendation on heterogeneous information network at home and abroad, this paper conducts bibliometric analysis and visual analysis with SATI, Ucinet, NetDraw, and SPSS to obtain the current research focuses and progress. According to the clustering results of literature keywords, the previous research are mainly based on clustering, random walk, meta-path, matrix factorization, network embedding algorithms, and applied in academic research, points of interest, Web services, social friends, patent trading, news and other recommendation scenarios. There is still a large development space for recommendation research based on heterogeneous information network. Future research can be carried out in dynamic recommendation, deep network representation learning and wider applications.

Key words: heterogeneous information network;recommendation;meta-path, network embedding