Modeling dynamic hierarchical intents for session-based recommendation

被引：0
|
作者：
Zhang M. [1 ,2 ]
Guo C. [1 ,2 ]
Pan M. [1 ,2 ]
Jin J. [1 ,2 ]
Xin Z. [1 ,2 ]
Fang J. [1 ]
Chen S. [3 ]
机构：
[1] Institute of Computing Technology, Chinese Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
[3] Joint Operations College, National Defense University, Shijiazhuang
来源：
High Technology Letters | 2022年 / 32卷 / 04期
关键词：
Dynamic convolution; Dynamic user interest; Hierarchical intention; Recommender system; Session-based recommender system;
D O I：
10.3772/j.issn.1002-0470.2022.04.005
中图分类号：
学科分类号：
摘要：
To solve the problem of single and static representation of user interests when modeling user preferences in current session-based recommender system methods, a dynamic hierarchical intention learning network is proposed, which considers both the multi-layer intentions and dynamic user behaviors. Two modules, dynamic convolution neural network and interest cluster gate, are designed to extract users' specific granularity intention in each layer. In addition, a constraint loss function is proposed to ensure the hierarchy of user intention. The final session representation incorporates multiple granularity intentions for recommendation. Extensive experiments on three real datasets show that the model outperforms other session-based recommendation methods in both accuracy and diversity. © 2022, Editorial Department of the Journal of Chinese High Technology Letters. All right reserved.
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收藏
页码：367 / 378
页数：11
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