Hybrid Recommendation System for Visual Novels Using Content and Collaborative Filtering

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.