[1]包 晨,袁卫华,戴久乾,等.基于通道注意力的神经协同过滤推荐算法[J].计算机技术与发展,2023,33(07):173-180.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 026]
　BAO Chen,YUAN Wei-hua,DAI Jiu-qian,et al.Neural Collaborative Filtering Recommendation Algorithm Based on Channel Attention[J].,2023,33(07):173-180.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 026]
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基于通道注意力的神经协同过滤推荐算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年07期
页码:
173-180
栏目:
人工智能
出版日期:
2023-07-10

文章信息/Info

Title:
Neural Collaborative Filtering Recommendation Algorithm Based on Channel Attention
文章编号:
1673-629X(2023)07-0173-08
作者:
包 晨; 袁卫华; 戴久乾; 张志军*
山东建筑大学 计算机科学与技术学院,山东 济南 250101
Author(s):
BAO Chen; YUAN Wei-hua; DAI Jiu-qian; ZHANG Zhi-jun*
School of Computer Science and Technology,Shandong Jianzhu University,Jinan 250101,China
关键词:
协同过滤; 通道注意力机制; 卷积神经网络; 广义矩阵分解; 推荐系统
Keywords:
collaborative filtering;  channel attention mechanism;  convolutional neural network;  generalized matrix factorization;  recommendation system
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 07. 026
摘要:
现有的协同过滤推荐算法使用表示学习方法和匹配函数学习的方法来匹配用户喜欢的物品,但这不能充分表达用户对不同物品的真实偏好,且这些模型并不能有效捕获用户和物品交互时嵌入维度之间的相关性。 为此,该文提出基于通道注意力的神经协同过滤模型 NCFCA( Neural Collaborative Filtering based on Channel Attention)。 首先,在网络中通过注意力机制对不同的物品分配不同的权重,来影响用户对物品的偏好程度;其次,模型利用卷积神经网络来提升用户和物品的关联性,并在卷积神经网络中加入通道注意力机制来挖掘丰富的语义信息;最后,利用广义矩阵分解方法来缓解因用户物品交互产生的数据稀疏问题并且将三个不同的模块( A -MLP、E -CNN、GMF) 融合在一起。 在 MovieLens 1M 和Lastfm 数据集上的大量实验表明,NCFCA 模型的准确率有不同程度的提高,表现出较为优越的推荐性能。
Abstract:
Existing collaborative filtering recommendation algorithms use representation learning methods and matching function learningmethods to match users’ favorite items,but they cannot fully express users’real preferences for different items, and these models cannoteffectively capture the correlation between the embedded dimensions in the interaction between users and items. Therefore,we propose aneural collaborative filtering model based on channel attention ( NCFCA) . Firstly,the attention mechanism is used to assign differentweights to different items in the network to influence the user’s preference degree of items. Secondly,the model uses convolutionalneural network to improve the correlation between users and items,and adds channel attention mechanism to the convolutional neuralnetwork to mine rich semantic information. Finally, the generalized matrix factorization method is used to alleviate the data sparsityproblem caused by user item interaction and the three different modules ( A-MLP,E-CNN,GMF) are fused together. A large number of experiments on MovieLens 1M and Lastfm datasets show that the accuracy of NCFCA model has been improved to varying degrees,showing superior recommendation performance.

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更新日期/Last Update: 2023-07-10