Abstract: Heterogeneous information network(HIN) is widely used in recommender systems since its rich semantic and structu-ral information.Although the HIN and the network embedding have achieved good results in recommender systems,the local feature amplification,the interaction of embedding vectors,and the multi-embedding aggregation methods have not been fully consi-dered.To overcome these problems,a new multi-embedding fusion recommendation(MFRec) model is proposed.Firstly,object-contextual representation network is introduced to both branches of user and node representation learning to amplify local features and enhance the interaction of neighbor nodes.Subsequently,the dilated convolution and the spatial pyramid pooling are introduced to the meta-paths learning to obtain multi-scale information and enhance the representation of meta-paths.In addition,the multi-embedding fusion module is introduced to better carry out the embedding fusion of users,items and meta-paths.The interaction between embeddings is carried out in a fine-grained way,and the different importance of each feature is emphasized.Finally,experimental results on two public recommendation system datasets show that the proposed MFRec has better performance than other existing top-N recommendation models.

Key words: Heterogeneous information network, Recommender system, Top-N recommendation, Multi-embedding fusion, Attention mechanism