Tutorials

New Paths in Music Recommender Systems Research

by Markus Schedl (Johannes Kepler University Linz, Austria), Peter Knees (Vienna University of Technology, Austria) and Fabien Gouyon (Pandora Inc., USA)

In the RecSys community, music is too often treated as “just another item”. Yet, the particularities of music data and its multiple modalities open many opportunities, e.g., to leverage content-based audio features or to build comprehensive listener models that go beyond simple user-item interactions. Furthermore, since it is now increasingly more common for a music listener to simply stream music rather than to purchase and own it, today’s music recommenders need to focus on recommending a listening experience. Algorithms that produce a one-shot recommendation for the purpose of a track or album purchase are no longer of central importance. As a consequence, Music Recommender System (MRS) research has to face a wide range of challenges, such as sequential recommendation, or conversational and contextual recommendation.

This introductory tutorial incorporates both academic and industrial points of view on latest developments in music recommendation research, presenting challenges and solutions. The content will be organized with respect to three use cases: playlist generation, context-aware music recommendation, and recommendation in the creative process of music making. In addition, we will discuss the implications of recent MRS technologies on actors, other than the listener, in the rich and complex music industry ecosystem (e.g., labels, music makers and producers, concert halls, advertisers). No particular prerequisite knowledge or skills are required from the audience, other than a very basic understanding of the main concepts in recommender systems. Accompanying the tutorial, we will publish a comprehensive set of slides, including references to state-of-the-art work and open implementations of several of the presented techniques. This will be available here.

Date

Monday, Aug 28, 2017, 16:15-18:00

Location

tba

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