《计算机应用研究》|Application Research of Computers

基于多头注意力机制和位置信息的xDeepFM推荐模型

xDeepFM recommendation model based on multi-head attention mechanism and location information

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作者 牛路帅,彭龑
机构 四川轻化工大学 自动化与信息工程学院,四川 宜宾 644000
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文章编号 1001-3695(2021)10-030-3055-05
DOI 10.19734/j.issn.1001-3695.2021.03.0088
摘要 为了解决推荐模型中无法挖掘用户兴趣多样性和捕捉用户行为序列之间的顺序信息,以及交互发生在元素级并非特征向量之间等问题,提出一种基于多头注意力机制和位置信息的xDeepFM推荐模型(extreme deep multiple attention and location information factorization machine,xDMALFM)。首先通过多头注意力机制进行不同子空间的特征深度提取,然后利用位置信息去捕捉用户行为序列之间的顺序关系。最后,利用三个公开数据集进行对比实验,以AUC指标进行评估。实验结果表明所提算法相比xDeepFM模型具有更好的推荐性能,验证了其有效性与可行性。
关键词 推荐算法; 深度学习; 位置信息; 多头注意力机制; xDeepFM
基金项目 四川省科技厅重点研发项目(19ZDYF1078)
自贡市科技局科技计划资助项目(2018GYCX33)
本文URL http://www.arocmag.com/article/01-2021-10-030.html
英文标题 xDeepFM recommendation model based on multi-head attention mechanism and location information
作者英文名 Niu Lushuai, Peng Yan
机构英文名 College of Automation & Information Engineering,Sichuan University of Light Chemical Technology,Yibin Sichuan 644000,China
英文摘要 In order to solve the problem that the recommendation model cannot mine the diversity of user interest and capture the order information between the sequence of user behavior, and the interaction occurs at the element level rather than between the feature vectors, etc., based on multiple attention mechanism and location information, this paper proposed the xDMALFM. Firstly, it extracted the feature depth from different subspaces by using the multi-head attention mechanism, and then location information could capture the sequential relationship between user behavior sequences. Finally, it used three public datasets to conduct comparative experiments and the AUC index to evaluate the results. The experimental results show that the proposed algorithm has better recommendation performance than the xDeepFM model and show the effectiveness and feasibility of the proposed algorithm.
英文关键词 recommendation algorithm; deep learning; location information; multiple attention mechanism; xDeepFM
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收稿日期 2021/3/30
修回日期 2021/5/8
页码 3055-3059
中图分类号 TP391
文献标志码 A