In plain words: Music users and items are placed in a curved space where distance from the center tracks hierarchy level, with each link's reliability estimated from data. In an A/B test on personalized playlists, this beat the usual flat-space model by a large, statistically significant margin.
Abstract · Music Recommendations in Hyperbolic Space: An Application of Empirical Bayes and Hierarchical Poincaré Embeddings
Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has suggested that a hyperbolic Poincaré ball may be more well suited to representing multiple entity types, and in particular, hierarchies. We describe a novel method to embed a hierarchy of related music entities in hyperbolic space. We also describe how a parametric empirical Bayes approach can be used to estimate link reliability between entities in the hierarchy. Applying these methods together to build personalized playlists for users in a digital music service yielded a large and statistically significant increase in performance during an A/B test, as compared to the Euclidean model.
Tim Schmeier, Sam Garrett, Joseph Chisari, Brett Vintch
arXiv:1907.12378 · cs.IR, cs.LG, stat.ML · submitted Jul 24, 2019
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