Authors
Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, Jian Wu
Publication date
2018/1/1
Journal
IJCAI international joint conference on artificial intelligence
Description
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. 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 itemitem interactions through a linear way which limits the capability of model. To this end, in this paper, we propose a novel two-layer hierarchical attention network, which takes the above properties into account, to recommend the next item user might be interested. Specifically, the first attention layer learns user long-term preferences based on the historical purchased item representation, while the second one outputs final user representation through coupling user long-term and short-term preferences. The experimental study demonstrates the superiority of our method compared with other state-of-the-art ones.
Total citations
20182019202020212022202320242436192688343
Scholar articles
H Ying, F Zhuang, F Zhang, Y Liu, G Xu, X Xie, H Xiong… - IJCAI international joint conference on artificial …, 2018