Deep Time-Aware Attention Neural Network for Sequential Recommendation

Qiang Hua (), Liyou Chen (), Chunru Dong (), Pan Li () and Feng Zhang
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Qiang Hua: Hebei Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information Science, Hebei University, Baoding 071002, P. R. China
Liyou Chen: Department of Mathematics and Information Science, Hebei University, Baoding 071002, P. R. China
Chunru Dong: Department of Mathematics and Information Science, Hebei University, Baoding 071002, P. R. China
Pan Li: Department of Mathematics and Information Science, Hebei University, Baoding 071002, P. R. China
Feng Zhang: Department of Mathematics and Information Science, Hebei University, Baoding 071002, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2023, vol. 40, issue 05, 1-22

Abstract: For click-through rate (CTR) prediction tasks, a good prediction performance can be obtained by full explorations of both user behavior and item behavior. Since user’s interests have a great influence on user’s behaviors, it is very important to learn users’ intrinsic interests according to their behaviors. User interests are not only diverse but also in dynamic change. However, the dynamics of user interests’ change are not fully taken into account by the majority of current CTR models. The latest sequential recommendation algorithm ignores the subjectivity of users when it uses a two-layer recurrent neural network to model the item behavior from the perspective of the evolution of items. In this work, we propose a recurrent neural network model called DTIAN (Deep Time-Aware Interest Attention Network) to address these issues. By leveraging the user behaviors and the corresponding temporal information, DTIAN captures user interests and intent changes to the target item. Therefore, the users’ recent interests are enhanced compared to early interests with the attention mechanism. In addition, each module of the proposed model can be plugged into other mainstream models to improve the performance of current models. The experimental results show that the proposed DTIAN can outperform the current popular CTR prediction models slightly and significantly reduce the training time, which makes it possible to implement lightweight models.

Keywords: Recommendation system; recurrent neural network; attention mechanism; user behavior (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0217595923400201

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