Abstract: In view of the problems of sparsity and underutilization of the heterogeneity of auxiliary information faced by current collaborative filtering methods and the advantages of heterogeneous information networks ( HIN) in modeling complex heterogeneous information，a HIN based tightly coupled recommendation model ( HTCRec) was proposed in this paper． It utilizes the heterogeneous information network embedding and a tightly coupled collaborative filtering framework to carry out personalized recommendation． Firstly，it aggregates meta-paths in a HIN and their corresponding path instances． Then it uses the attention mechanism to represent the auxiliary information of the target users or items in terms of the embedding of the respective aggregation meta-paths． At last，the meta-path is explicitly incorporated into the tightly coupled interaction model for personalized recommendation． The experimental results of the real data sets show that compared with the state-of-the-art recommendation models，the HTCRec model has better recommendation performance and effectively alleviates the problem of data sparsity．

Key words: tightly coupled recommendation model, heterogeneous information network, matrix factorization, network embedding, attention mechanism