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

Mingxin Gan (), Xiongtao Zhang () and Wenyu Wang ()
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Mingxin Gan: University of Science and Technology Beijing
Xiongtao Zhang: University of Science and Technology Beijing
Wenyu Wang: University of Science and Technology Beijing

Electronic Commerce Research, 2025, vol. 25, issue 4, No 16, 2795-2823

Abstract: 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.

Keywords: Sequence learning; Movie recommendation; Dual-side evolution; User interest; Movie attraction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10660-023-09770-w

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