Hybrid Recommendation System for Visual Novels Using Content and Collaborative Filtering
1
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032730534
Journal Title
Icsec 2025 29th International Computer Science and Engineering Conference 2025
Start Page
293
End Page
297
Rights Holder(s)
SCOPUS
Bibliographic Citation
Icsec 2025 29th International Computer Science and Engineering Conference 2025 (2025) , 293-297
Suggested Citation
Unjindamanee S., Poopradit J., Kaewmanee P., Sujipisut P., Phienthrakul T., Hunchangsith K. Hybrid Recommendation System for Visual Novels Using Content and Collaborative Filtering. Icsec 2025 29th International Computer Science and Engineering Conference 2025 (2025) , 293-297. 297. doi:10.1109/ICSEC67360.2025.11298027 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115853
Title
Hybrid Recommendation System for Visual Novels Using Content and Collaborative Filtering
Author's Affiliation
Corresponding Author(s)
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Abstract
This paper presents a hybrid recommendation system for visual novel discovery, integrating content-based filtering and collaborative filtering within the Machine Learning Visual Novel Recognition and Recommendation (MLVNR2) platform. The system combines hierarchical tag metadata and user interaction histories to generate a ranked list of relevant visual novels. The hybrid strategy blends content and collaborative signals while safeguarding semantic relevance. A user study involving thirty-one participants achieved a perfect top-3 hit rate and an average top-10 reciprocal rank of 98.4%, while additionally revealing strong novelty discovery, with 83.9% of recommendations introducing users to unfamiliar titles. More than 80% of participants reported positive satisfaction. In terms of precision at cut-offs, the system delivered 84.9% for the top three results, 76.8% for the top five, and 61.6% for the top ten. These findings demonstrate the effectiveness of combining semantic metadata similarity with user behaviour patterns for personalized visual novel recommendation.