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Song embeddings are a key component of most music recommendation engines. In this work, we study the hyper-parameter optimization of behavioral song embeddings based on Word2Vec on a selection of downstream tasks, namely next-song recommendation, false neighbor rejection, and artist and genre clustering. We present new optimization objectives and metrics to monitor the effects of hyper-parameter optimization. We show that single-objective optimization can cause side effects on the non optimized metrics and propose a simple multi-objective optimization to mitigate these effects. We find that next-song recommendation quality of Word2Vec is anti-correlated with song popularity, and we show how song embedding optimization can balance performance across different popularity levels. We then show potential positive downstream effects on the task of play prediction. Finally, we provide useful insights on the effects of training dataset scale by testing hyper-parameter optimization on an industry-scale dataset.

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