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Dual-evolution: a deep sequence learning model exploring dual-side evolutions for movie recommendation

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Abstract

Influenced by external environment or individual cognition, interests of a user and attractions of a movie keep evolving over times in the movie recommendation scenario. However, existing studies on movie recommendation ignored the evolutions of user interests and movie attractions, which leads to suboptimal recommendation performance. In order to improve movie recommendation, we developed a deep sequence learning model, namely Dual-Evolution, to simultaneously explore evolutions of user interests and movie attractions for movie recommendation. Specifically, Dual-Evolution first extracted temporary user interests and movie attractions by learning behavior sequences of the user and the movie. And then, Dual-Evolution modeled evolution processes of temporary user interests and movie attractions by considering not only their sequential association but also their relative importance. Further, we conducted experiments for two tasks on two benchmark real-world datasets. Experimental results indicate that Dual-Evolution significantly outperforms mainstream movie recommendation methods in the movie rating prediction as well as the top-N recommendation, which inspires online movie platforms more reasonably designs movie recommender systems.

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Notes

  1. https://github.com/sidooms/movietweetings.

  2. http://moviedata.csuldw.com/.

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Acknowledgements

This studies was partly supported by the National Natural Science Foundation of China (Nos. 72271024, 71871019, 71471016).

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  1. Xiongtao Zhang and Wenyu Wang have contributed equally to this work.

Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Haidian District, Beijing, 100083, Beijing, China

    Mingxin Gan, Xiongtao Zhang & Wenyu Wang

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  1. Mingxin Gan
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Contributions

MG Conceptualization, Supervision, Funding acquisition. XZ Conceptualization, Methodology, Software, Validation, Writing—Original Draft, Writing—review and editing. WW Conceptualization, Data Curation, Writing—Original Draft.

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Correspondence to Mingxin Gan.

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Gan, M., Zhang, X. & Wang, W. Dual-evolution: a deep sequence learning model exploring dual-side evolutions for movie recommendation. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09770-w

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