1. 基于自适应图卷积注意力神经协同推荐算法.
- Author
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杜雨癶, 王 巍, 张 闯, 郑小丽, 苏嘉涛, and 王杨洋
- Subjects
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CONVOLUTIONAL neural networks , *RECOMMENDER systems , *MATRIX decomposition , *INFORMATION overload , *INTERNET , *GRAPH algorithms , *ELECTRONIC books - 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]
- Published
- 2022
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