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Long- and short-term collaborative attention networks for sequential recommendation

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Abstract

Sequential recommendation models the users’ historical interaction sequence and predicts which item the user will click next. To better capture the users’ hobbies, most models utilize the users’ interaction sequence to capture the users’ long-term hobbies, while ignoring the users’ short-term intentions. Recently, some work has focused on capturing users’ long-term hobbies and short-term intentions to predict the next item recommendation. However, they only consider the information about the user’s interaction sequence and ignore the information about the collaboration between different user interaction sequences. This paper proposes a Long- and Short-Term Collaborative attention network for Sequential Recommendation (LSTCSR) to better capture and integrate users’ long-term and short-term hobbies. Specifically, we construct an item–item graph with the interaction sequences of different users to obtain information on the collaboration between items in different sequences. It then uses a self-attention network to capture the users’ long-term hobbies and utilizes convolutional filters of different sizes to capture the users’ multiple short-term intentions. Finally, the users’ long-term hobbies and short-term intentions are integrated through the collaborative information of the item–item graph to predict the next item recommendation. Experiments on 3 public benchmark datasets show that LSTCSR model outperforms several state-of-the-art methods, further demonstrating the effectiveness of the LSTCSR model.

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Data Availability

Links to datasets during the current study period are available at https://www.kaggle.com/datasets/prajitdatta/movielens-100k-dataset, https://www.kaggle.com/datasets/odedgolden/movielens-1m-dataset and https://www.kaggle.com/datasets/deovcs/amazon-dataset.

Code Availability

Not applicable.

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Acknowledgements

The National Natural Science Foundation of China (No. 61772295, 61572270, and 61173056). The Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202000501). Chongqing Technology Innovation and application development special general project (cstc2020jscx-lyjsAX0002). Chongqing Technology Foresight and system innovation project (cstc2021jsyj-yzysbAX0011). Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS--202208).

Funding

This research was supported by the National Natural Science Foundation of China (No. 61772295, 61572270, and 61173056). The Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202000501). Chongqing Technology Innovation and application development special general project (cstc2020jscx-lyjsAX0002). Chongqing Technology Foresight and system innovation project (cstc2021jsyj-yzysbAX0011). Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS--202208).

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Authors and Affiliations

  1. College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China

    Yumin Dong, Yongfu Zha & Yongjian Zhang

  2. College of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541000, China

    Xinji Zha

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  1. Yumin Dong
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  2. Yongfu Zha
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Contributions

Yongfu Zha is responsible for algorithm design and manuscript writing. Yumin Dong is responsible for the theory proposal and checking the structure of the thesis. Xinji Zha and Yongjian Zhang are responsible for data collection and experimental validation.

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Correspondence to Yumin Dong.

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Dong, Y., Zha, Y., Zhang, Y. et al. Long- and short-term collaborative attention networks for sequential recommendation. J Supercomput 79, 18375–18393 (2023). https://doi.org/10.1007/s11227-023-05348-3

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