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基于自适应图卷积注意力神经协同推荐算法.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . Jun2022, Vol. 39 Issue 6, p1760-1766. 7p. - Publication Year :
- 2022
-
Abstract
- With the rapid development of the Internet, the recommendation system can handle the problem of information overload. Due to many problems in traditional recommendation systems, they can' t handle the discovery of hidden information. This paper proposed an adaptive graph convolution attention neural collaborative filtering recommendation model ( ANGCACF) . Firstly, graph convolution neural network obtained the user-item interaction figure and adaptively aggregated the user-item feature information. Secondly, the model added adaptive extended data to solve the data sparsity for the user-item feature information and used the attention mechanism to redistribute the weight to the user-item feature information and the adaptive extended data. Finally, it obtained the final recommendation result by using the algorithm framework of collaborative filtering based on matrix decomposition. Experiments on MovieLens-lM, MovieLens-lOOK and Amazon-book show that the algorithm is superior to the baseline method in five indexes : precision, recall, Mrr, hit and NDCG. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 39
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 157623983
- Full Text :
- https://doi.org/10.19734/j.issn.1001-2021.11.0631