Groove radio: A Bayesian hierarchical model for personalized playlist generation

Shay Ben-Elazar, Gal Lavee, Noam Koenigstein, Oren Barkan, Hilik Berezin, Ulrich Paquet, Tal Zaccai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a "seed" artist and the listener's user ID. Playlist generation may be informed by a number of information sources in- cluding: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary de- pending on the specific combination of user and seed artist. The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a per- sonalization component for user-specific preferences. Em- pirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.

Original languageEnglish
Title of host publicationWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
Pages445-453
Number of pages9
ISBN (Electronic)9781450346757
DOIs
StatePublished - 2 Feb 2017
Externally publishedYes
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: 6 Feb 201710 Feb 2017

Publication series

NameWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

Conference

Conference10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/TerritoryUnited Kingdom
CityCambridge
Period6/02/1710/02/17

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