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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Tag-based recommendation system using bi-clustering

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pp. 2021–2029Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2293XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2293
Pages:
2021–2029

Abstract

With the onset of the new age, consumers are overwhelmed by the many choices of movies that can be watched. A solution gives rise to the need for a recommendation system (RecSys) that will suggest a title to the user based on user requirements. The new RecSys must be able to address the issues that exist within the plethora of general RecSys. In this work, we have implemented a novel Tag-based RecSys to improve the quality and usefulness of recommendations based on user information and movie tags. This paper proposes a bi-clustering approach for simultaneously clustering movies and users. This work is implemented using the MovieLens datasets. In trying to gauge the efficacy of the new RecSys, the final RMSE of our Tag-based RecSys is 0.9984, and the MAE is 0.8450. The system also produces more personalised results for users by personalising scores. The system evaluation shows considerably positive results.

Keywords

Tag-based recommendation systemMovies tag analysisTag-based movie recommender

Subject Classifications

Primary 93A30Secondary 49K15

References

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