Deep Multi-Objective Multi-Stakeholder Music Recommendation

47 Pages Posted: 20 May 2021 Last revised: 11 Apr 2022

See all articles by Moshe Unger

Moshe Unger

New York University (NYU)

Pan Li

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

Maxime C. Cohen

Desautels Faculty of Management, McGill University

Brian Brost

Spotify

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 for a single stakeholder (i.e., the customer) based on customer-product interaction records. In this paper, we argue that for two-sided marketplaces such as music streaming platforms, it is more beneficial to consider multiple objectives for multiple stakeholders in the utility function for recommendations. We propose a novel recommender system design that aims at satisfying multiple---potentially conflicting---objectives of consumers and vendors. Specifically, we propose a "two-tower" neural network model for music recommendation that (a) learns each stakeholder objective in a different tower, (b) shares the latent information that was learned in each tower between the middle layers to predict each stakeholder objective, and (c) aggregates the predicted objectives using optimization methods to generate rating-based recommendations. In our model, we focus on user satisfaction objectives, such as saves, likes, and degrees of engagement with songs; and artist satisfaction objectives, such as acquiring new fans. We examine the performance of our proposed model on two proprietary industrial-scale datasets provided by Spotify and one 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 multi-stakeholder methods.

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

Suggested Citation

Unger, Moshe and Li, Pan and Cohen, Maxime C. and Brost, Brian and Tuzhilin, Alexander, Deep Multi-Objective Multi-Stakeholder Music Recommendation (May 18, 2021). NYU Stern School of Business Forthcoming, 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

Pan Li

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

44 West Fourth Street
New York, NY 10012
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

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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