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A Self-Evaluating Collaborative Filtering Recommendation Algorithm: A Case Study of Anime Recommendations
- 1 School of Data Sciences, Brunel University London, London, UK
* Author to whom correspondence should be addressed.
Abstract
This paper introduces a collaborative filtering recommendation algorithm aimed at addressing the issues of information insufficiency and the mismatch of user personalization needs in anime recommendations. Firstly, we review relevant literature to explore the application of collaborative filtering algorithms in recommendation systems and previous research findings. Then, we detail the design and implementation of collaborative filtering algorithms based on anime and user data, calculating similarities between anime and between users respectively for recommendation purposes. Finally, a self-evaluation is conducted to achieve optimal recommendation performance. Experimental results show that when recommending 10 anime titles, the ROC analysis results indicate a high level of precision, with both algorithms performing well in terms of accuracy.
Keywords
recommendation system, collaborative filtering, ROC analysis, personalized anime recommendation.
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Cite this article
Zhang,B. (2024). A Self-Evaluating Collaborative Filtering Recommendation Algorithm: A Case Study of Anime Recommendations. Theoretical and Natural Science,53,98-105.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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