Deep Multi-Objective Multi-Stakeholder Music Recommendation

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