Annular-Graph Attention Model for Personalized Sequential Recommendation

Sequential recommendations aim to predict the user's next behaviors items based on their successive historical behaviors sequence. It has been widely applied in lots of online services. However, current sequential recommendations use the adjacent behaviors to capture the features of the sequence, ignoring the features among nonadjacent sequential items and the summarized features of the sequence. To address the above problems, in this paper, we propose an annular-graph attention based sequential recommendation (AGSR) model by exploring user's long-term and short-term preferences for the personalized sequential recommendation. For user's short-term preferences, AGSR builds an annular-graph on the sequence of user behavior. Then, AGSR proposes an annular-graph attention applying on the sub annular-graph to explore local features and applying annular-graph attention on entire annular-graph to explore the global features and the skip features. For user's long-term preferences, the latent factor model are introduced in AGSR. The experimental results on two public datasets show that our model outperforms the state-of-the-art methods.

Automated summary

This paper proposes an annular-graph attention based sequential recommendation model that explores user's long-term and short-term preferences for personalized recommendations. Short-term preferences are captured by building an annular-graph and applying graph attention for local and global features, while long-term preferences are handled through the introduction of latent factor models. Experimental results show that the model outperforms current state-of-the-art methods on two public datasets.