Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning

arXiv:2402.16299v1 Announce Type: cross
Abstract: Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numerous recommendation algorithms are designed to provide users with a personalized array of information tailored to their preferences. Nevertheless, excessive personalization can confine users within a "filter bubble". Consequently, achieving the right balance between accuracy and diversity in recommendations is a pressing concern. To address this challenge, exemplified by music recommendation, we …

abstract algorithms array arxiv cs.ir cs.lg embedding filter hypergraph inappropriate information music personalization personalized recommendation recommendation algorithms recommender systems serve systems type via