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Music Cold-Start Matching with BCL by yatingliu2022 is a document available to read on EtoBox.

What is Music Cold-Start Matching with BCL about?

This paper introduces a novel approach to Music Cold-Start Matching, which aims to retrieve songs with similar audiences for cold-start song requests. The authors propose a Bootstrapping Contrastive Learning (BCL) paradigm to enhance song representation learning and a Clustering-based Audience Targeting (CAT) method to accurately locate target audiences. Extensive experiments demonstrate the effectiveness of their methods, which have been deployed on NetEase Cloud Music, impacting millions of users.

Author

yatingliu2022

Language

EN