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Sequential Recommender System based on Hierarchical Attention Networks
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing methods often neglect that user long-term preference keep evolving over time, and building a static representation for user general taste may not adequately reflect the dynamic characters. Moreover, they integrate user-item or item-item
doi:10.24963/ijcai.2018/546
dblp:conf/ijcai/YingZZLXXX018
fatcat:pzi6knhz4nfazd7o3igltcbpcy