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People-Powered Music: Using User-Generated Tags and Structure in Recommendations
Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches
Learning to rank music tracks using triplet loss
Most music streaming services rely on automatic recommendation algorithms to
exploit their large music catalogs. These algorithms aim at retrieving a ranked
list of music tracks based on their similarity with a target music track. In
this work, we propose a method for direct recommendation based on the audio
content without explicitly tagging the music tracks. To that aim, we propose
several strategies to perform triplet mining from ranked lists. We train a
Convolutional Neural Network to learn the similarity via triplet loss. These
different strategies are compared and validated on a large-scale experiment
against an auto-tagging based approach. The results obtained highlight the
efficiency of our system, especially when associated with an Auto-pooling
layer
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