Item-Item Music Recommendations With Side Information
Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of content makes it difficult for a user to find relevant tracks. Relevant recommendations are therefore crucial for a good user experience. Here we present a method to compute track-track similarities using collaborative filtering signals with side information. On a data set from music streaming service SoundCloud, the method here outperforms the widely adopted implicit matrix factorization technique. The implementation of our method is open sourced and can be applied to related item-item recommendation tasks with side information.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringDiversitySimilar 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 RecommendationsThe Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context …
Music RecommendationRecommendation SystemsExploring Longitudinal Effects of Session-based Recommendations
Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an ongoing session. The lack of long-term pr…
Recommendation SystemsRe-RankingSession-Based RecommendationsInterface Design to Mitigate Inflation in Recommender Systems
Recommendation systems rely on user-provided data to learn about item quality and provide personalized recommendations. An implicit assumption when aggregating ratings into item quality is that ratings are strong indicat…
Recommendation SystemsArtist and style exposure bias in collaborative filtering based music recommendations
Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across different styles. In this on-going work we…
Collaborative FilteringMusic Recommendation