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Annular-Graph Attention Model for Personalized Sequential Recommendation
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AI
DOI：10.1109/TMM.2021.3097186.png)
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Abstract
En 中文
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.
Keywords:
Recommender systems
Collaboration
Recurrent neural networks
Deep learning
Computational modeling
Measurement
Markov processes
Attention mechanism
graph attention
personalized recommendation
sequential recommendation
user preferences
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