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We study a particular matching task we call Music Cold-Start Matching. Inshort, given a cold-start song request, we expect to retrieve songs withsimilar audiences and then fastly push the cold-start song to the audiences ofthe retrieved songs to warm up it. However, there are hardly any studies doneon this task. Therefore, in this paper, we will formalize the problem of MusicCold-Start Matching detailedly and give a scheme. During the offline training,we attempt to learn high-quality song representations based on song contentfeatures. But, we find supervision signals typically follow power-lawdistribution causing skewed representation learning. To address this issue, wepropose a novel contrastive learning paradigm named Bootstrapping ContrastiveLearning (BCL) to enhance the quality of learned representations by exertingcontrastive regularization. During the online serving, to locate the targetaudiences more accurately, we propose Clustering-based Audience Targeting (CAT)that clusters audience representations to acquire a few cluster centroids andthen locate the target audiences by measuring the relevance between theaudience representations and the cluster centroids. Extensive experiments onthe offline dataset and online system demonstrate the effectiveness andefficiency of our method. Currently, we have deployed it on NetEase CloudMusic, affecting millions of users. Code will be released in the future.

Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching