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Preference Elicitation for Music Recommendations
Ofer Meshi · Jon Feldman · Li Yang · Ben Scheetz · Yanli Cai · Mohammad Hossein Bateni · Corbyn Salisbury · Vikram Aggarwal · Craig Boutilier

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in The Many Facets of Preference-Based Learning »
Event URL: https://openreview.net/forum?id=Lwiomr4mIS »

The cold start problem in recommender systems (RSs) makes the recommendation of high-quality content to new users difficult. While preference elicitation (PE) can be used to “onboard” new users, PE in music recommendation presents unique challenges to classic PE methods, including: a vast item (music track) corpus, considerable within-user preference diversity, multiple consumption modes (or downstream tasks), and a tight query “budget.” We develop a PE framework to address these issues, where the RS elicits user preferences w.r.t. item attributes (e.g., artists) to quickly learn coarse-grained preferences that cover a user’s tastes. We describe heuristic algorithms that dynamically select PE queries, and discuss experimental results of these methods onboarding new users in YouTube Music.

Author Information

Ofer Meshi (Google)
Jon Feldman (Google, Inc)
Li Yang (Google)
Ben Scheetz (Google)
Yanli Cai
Mohammad Hossein Bateni (Google Research)
Corbyn Salisbury (YouTube)
Vikram Aggarwal
Craig Boutilier (Google)

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