Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
Advanced music recommendation systems are being introduced along with the development of machine learning. However, it is essential to design a music recommendation system that can increase user satisfaction by understanding users' music tastes, not by the complexity of models. Although several studies related to music recommendation systems exploiting negative preferences have shown performance improvements, there was a lack of explanation on how they led to better recommendations. In this work, we analyze the role of negative preference in users' music tastes by comparing music recommendation models with contrastive learning exploiting preference (CLEP) but with three different training strategies - exploiting preferences of both positive and negative (CLEP-PN), positive only (CLEP-P), and negative only (CLEP-N). We evaluate the effectiveness of the negative preference by validating each system with a small amount of personalized data obtained via survey and further illuminate the possibility of exploiting negative preference in music recommendations. Our experimental results show that CLEP-N outperforms the other two in accuracy and false positive rate. Furthermore, the proposed training strategies produced a consistent tendency regardless of different types of front-end musical feature extractors, proving the stability of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMusic RecommendationRecommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation -- continuously providing new items within a single session in a contextually coherent mann…
Contrastive LearningMusic RecommendationSequential RecommendationSession-Based RecommendationsLeveraging the structure of musical preference in content-aware music recommendation
State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user's interest from his listening habits and similarities with other users' profiles. These approaches are agnostic to…
Collaborative FilteringMusic RecommendationRecommendation SystemsPersonalized Music Recommendation with Triplet Network
Since many online music services emerged in recent years so that effective music recommendation systems are desirable. Some common problems in recommendation system like feature representations, distance measure and cold…
Music RecommendationRecommendation SystemsTripletLeveraging Negative Signals with Self-Attention for Sequential Music Recommendation
Music streaming services heavily rely on their recommendation engines to continuously provide content to their consumers. Sequential recommendation consequently has seen considerable attention in current literature, wher…
Contrastive LearningMusic RecommendationSequential RecommendationGlobal and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems
Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-base…
Music RecommendationRecommendation Systems