2019/07/24 by Tim Schmeier, Timothy Schmeier, Sam Garrett +3 · 1 voice · 5 citations
Computer Science · Mathematics · #Advanced Text Analysis Techniques #Artificial intelligence #Computer science #Eigenvalues and eigenvectors #Embedding #Euclidean geometry #Hierarchy #Hyperbolic space #Mathematics #Matrix decomposition #Music and Audio Processing #Parametric statistics #Pure mathematics #Recommender Systems and Techniques #Space (punctuation) #Statistics #Theoretical computer science #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.1145/3298689.3347029
published as Thirteenth ACM Conference on Recommender Systems (RecSys '19), September 16--20, 2019, Copenhagen, Denmark
arxiv created 2019/07/24 · arxiv updated 2019/07/30 · openalex publication_date 2019/09/10 · openalex created_date 2022/07/28 · openalex updated_date 2026/08/05
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.