Sequential Recommendation Model Based on Temporal Convolution Attention Neural Network

被引：0
|
作者：
Du Y. [1 ]
Niu J. [1 ]
Wang L. [2 ]
Yan R. [1 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2022年 / 35卷 / 05期
关键词：
Long and Short Term Interest; Self-Attention Mechanism; Sequential Recommendation; Temporal Convolution Network;
D O I：
10.16451/j.cnki.issn1003-6059.202205008
中图分类号：
学科分类号：
摘要：
Sequential recommendation task aims to dynamically model user interests based on user-item interaction records for item recommendation. In sequential recommendation models, user behaviors are usually modeled as interests. The models only consider the order of user behaviors while ignoring the time interval information between users. In this paper, the time interval information of behavior sequences is taken as an important factor for prediction. A temporal convolution attention neural network model(TCAN) is proposed. In the word embedding layer, the sequential position information and time interval information are introduced, and a temporal convolutional network is designed to model the position information to obtain user's long-term preference features. In addition, the two-layer self-attention mechanism is adopted to model the association between items in the user's short-term behavior sequence, and the time interval information is fused to obtain the user's short-term interest. Finally, the global information of the training data is introduced through pre-training to improve the model recommendation performance. Experiments on three datasets show that the proposed model effectively improves recommendation performance. © 2022, Science Press. All right reserved.
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页码：472 / 480
页数：8
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