Research on improving movie recommendation algorithm based on user behavior

Yan Wang

doi:10.1117/12.3003936

11 October 2023 Research on improving movie recommendation algorithm based on user behavior

Yan Wang

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128000V (2023) https://doi.org/10.1117/12.3003936
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Most recommendation algorithms extract interest information from user behavior, but due to the rapid changes in user interest, freedom of user behavior, and poor rules of behavior data, the recommendation results are not ideal. Based on the above issues, combined with the strong regularity and high information accuracy of feature labels, a user behavior recommendation algorithm based on fused film feature labels is proposed, which introduces film feature labels and improves similarity measurement methods based on feature label weights. This algorithm establishes a user movie feature label matrix model based on movie labels in user behavior, constructs a new user preference feature model, and establishes a new similarity measurement method based on label weights to recommend matching movies based on user preference features. The experiment showed that on the Movielens dataset, the user coverage rate of the fusion film label algorithm reached 86.52%, and the accuracy and recall rate of recommendation results reached 58.89% and 35.59%, respectively, effectively improving the algorithm performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Wang "Research on improving movie recommendation algorithm based on user behavior", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128000V (11 October 2023); https://doi.org/10.1117/12.3003936

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KEYWORDS

Tunable filters

Data modeling

Matrices

Detection and tracking algorithms

Video

Computing systems

Design and modelling

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