Attention-aware metapath-based network embedding for HIN based recommendation

Heterogeneous information network (HIN) attracts increasing attention from the communities of recommender systems. HIN based recommendation methods can help overcome the difficulties of data sparsity and cold start. The majority of the existing HIN based recommendation methods use path-based semantic similarity between users and/or between items on HINs. However, the existing HIN based recommendation methods using metapath disregard the semantic differences among multiple metapaths (i.e., inter-metapaths) and the influence differences among neighbor pairs in each individual metapath (i.e., intra-metapaths). To solve these problems, we propose an attention-aware metapath-based network embedding for HIN based recommendation. To obtain additional semantic information, our method generates multiple metapath-based weighted homogeneous networks to model the auxiliary information of users and items of HIN. Thereafter, we design a novel self-attention integration to integrate multiple semantic information from multiple weighted homogenous information networks. Lastly, we utilize three deep neural network methods to model the implicit relations between users and items for the rating prediction task. Experimental results of three real-world datasets demonstrate that the proposed model outperforms existing state-of-the-art recommendation methods, solves the data sparsity problem, and models the multiple semantic information of users and items.

Automated summary

In this study, an attention-aware metapath-based network embedding method is proposed to address the issue of neglecting semantic differences between different metapaths in existing HIN based recommendation methods. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art recommendation methods, solves the data sparsity problem, and models the multiple semantic information of users and items effectively.