基于时间卷积注意力神经网络的序列推荐模型

doi:10.16451/j.cnki.issn1003-6059.202205008

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Abstract 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.

Key words： Sequential Recommendation Self-Attention Mechanism Temporal Convolution Network Long and Short Term Interest

Received: 14 January 2022

ZTFLH:

TP391.1

Fund:National Key R&D Program of China(No.2019YFC1906002), Beijing Natural Science Foundation(No.4212013)

Corresponding Authors: DU Yongping, Ph.D., professor. Her research interests include information retrieval, information extraction and natural language processing.

About author:: NIU Jinyu, master student. His research interests include recommendation system and knowledge distillation.
WANG Lulin, master, engineer. His research interests include intelligent information processing and recommendation system.
YAN Rui, master student. His research interests include natural language processing.

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Cite this article:

DU Yongping,NIU Jinyu,WANG Lulin等. Sequential Recommendation Model Based on Temporal Convolution Attention Neural Network[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(5): 472-480.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202205008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I5/472

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