Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs

Heterogeneous information networks (HINs) have seen rapid development and have attracted extensive attention because of their effectiveness in recommender systems. Although many existing models based on HINs for recommender systems have obtained good recommendation performance on account of their superior ability to process heterogeneous data and capture rich semantic information, there are still several problems. Firstly, the temporal relations among different nodes in meta-paths, which include users and items, are rarely considered in HINs. Secondly, the interactions among meta-paths, users, and items are similarly often overlooked. Thirdly, their ability to learn the heterogeneous information of users and items is limited. In view of the above problems, we propose a system for the temporal enhancement of top-N recommendations on HINs called TMRec. Specifically, we first adopted long short-term memory (LSTM) and residual self-attention (RSA) to process users and items and enhance the network’s ability to both learn the heterogeneous information in them and capture the temporal relations among them. Second, we designed a novel method for processing meta-paths, including deep perception self-attention (DPSA), max pooling, and L2-normalization, that can effectively obtain the temporal relations among different nodes in meta-paths. Third, we used collaborative attention to process meta-paths, users, and items to obtain their interactions. Finally, extensive experiments were conducted on four public datasets of recommender systems to verify the superiority of our method compared with state-of-the-art top-N recommendation models.