Deep Multi-Objective Multi-Stakeholder Music Recommendation

39 Pages Posted: 20 May 2021

See all articles by Moshe Unger

Moshe Unger

New York University (NYU)

Maxime C. Cohen

Desautels Faculty of Management, McGill University

Brian Brost

Spotify

Pan Li

New York University (NYU) - Department of Information, Operations, and Management Sciences

Alexander Tuzhilin

New York University (NYU) - Leonard N. Stern School of Business; New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: May 18, 2021

Abstract

Many successful recommendation approaches rely on the optimization of a single objective function and focus on predicting ratings by considering customer and product features. In this paper, we consider the multi-objective recommendation problem for several stakeholders and introduce a large-scale recommender system that aims at satisfying multiple, potentially conflicting, objectives of consumers and vendors. We propose a "two-tower" neural network model for music recommendation that: (a) learns each of the stakeholders' objectives in a different tower, (b) shares the latent information that was learned in each tower to predict each stakeholder's objective, and (c) aggregates the predicted objectives to generate rating-based recommendations. Specifically, we focus on the following criteria: user satisfaction objectives, such as saves, likes, and degrees of engagement with songs; and artist satisfaction objectives, such as acquiring new fans. We apply our proposed deep architecture to music recommendation and examine the performance of our model on two proprietary industrial-scale datasets provided by Spotify and on a public music listening history dataset from Last.fm. We find that our model is effective in solving the multi-objective problem and achieves the best performance trade-off for the two stakeholders (users and artists), when compared to the original single-objective model and state-of-the-art multi-objective and multi-stakeholder methods.

Keywords: recommendation, neural networks, multi-objective, multi-stakeholder, transfer learning

Suggested Citation

Unger, Moshe and Cohen, Maxime C. and Brost, Brian and Li, Pan and Tuzhilin, Alexander, Deep Multi-Objective Multi-Stakeholder Music Recommendation (May 18, 2021). Available at SSRN: https://ssrn.com/abstract=3848670 or http://dx.doi.org/10.2139/ssrn.3848670

Moshe Unger

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Maxime C. Cohen (Contact Author)

Desautels Faculty of Management, McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Brian Brost

Spotify ( email )

150 Greenwich St
New York, NY 10007
United States

Pan Li

New York University (NYU) - Department of Information, Operations, and Management Sciences ( email )

44 West Fourth Street
New York, NY 10012
United States

Alexander Tuzhilin

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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