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EMARec: a sequential recommendation with exponential moving average

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Abstract

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics.

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Data availability

The data used in this study are publicly available http://jmcauley.ucsd.edu/data/amazon/index_2014.html.

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Acknowledgements

Thank Prof. Ivan Lee for your valuable feedback on the revision of the paper. This work was supported in part by the National Natural Science Foundation of China under Grants 62072416, 61902361, 61672471, 61975187, and 61802352, in part by Key Research and Development Program of Shaanxi Province under Grant 2024GX-YBXM-545, in part by the Henan Key Research Project of Higher Education Institutions under Grant 22B520046, in part by Key Research and Development Special Project of Henan Province under Grants 221111210500, 232102211053, 222102210170, 222102110045, and 232102321069, in part by the Natural Science Foundation Project of Henan Province under Grant 222300420582, in part by the Doctoral Fund Project of Zhengzhou University of Light Industry under Grants 2020BSJJ030 and 2020BSJJ031, in part by the Mass Innovation Space Incubation Project under Grant 2023ZCKJ216, in part by the Data Science and Knowledge Engineering Team of Zhengzhou University of Light Industry, and in part by the innovation team of data science and knowledge engineering of Zhengzhou University of Light Industry under Grant 13606000032.

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Authors and Affiliations

  1. College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China

    Rui Chen, Zonglin Wang, Jianwei Zhang, Pu Li & Min Huang

  2. Henan Joint International Research Laboratory of Computer, Animation Implementation Technologies, Zhengzhou, 450001, China

    Rui Chen

  3. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Cundong Tang

  4. Faculty of Information and Engineering, Xuchang Vocational Technical College, Xuchang, 461000, China

    Jianwei Zhang

  5. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Xiangjie Kong

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  1. Rui Chen
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Correspondence to Rui Chen or Jianwei Zhang.

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Chen, R., Wang, Z., Tang, C. et al. EMARec: a sequential recommendation with exponential moving average. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09718-7

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