Back to Search
Start Over
基于胶囊图卷积的解缠绕会话感知推荐方法.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . Jan2023, Vol. 40 Issue 1, p122-128. 7p. - Publication Year :
- 2023
-
Abstract
- To address the problem of poor recommendation accuracy in the session recommendation model, this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation (CGCD) method.Specifically, it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels. Then, it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding.In addition, it used a multi-head attention mechanism to assign weights to each item in the session. Finally, it aggregated the item embedding with other items in the current session according to the assigned weights, and then generated an accurate session representation to achieve item recommendation. Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10, Pre@20, MRR@10 and MRR@20 by 5.17%, 2.99%, 6.56% and 2.94% on average of the recommended performance, which verifies the effectiveness and efficiency of the method in this paper. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CAPSULE neural networks
*DEEP learning
*RECOMMENDER systems
*MACHINE learning
Subjects
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 40
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 161285608
- Full Text :
- https://doi.org/10.19734/j.issn.1001-3695.2022.05.0283