Bridging Listeners with Artists: Deep Multi-Objective Multi-Stakeholder Music Recommendations

Abstract

Most music recommendation services operate by optimizing the objectives of a single stakeholder (i.e., the listener) based on music listening records. In this paper, we argue that it is more beneficial to consider the objectives of multiple stakeholders (artists and listeners) simultaneously in the utility function for music recommendations, which leads to significant improvements not only in the overall recommendation performance, but also in the welfare of artists and listeners separately. To achieve this goal, we propose a novel multi-objective multi-stakeholder recommendation framework, where we utilize state-of-the-art deep learning techniques to formulate multiple ``tower'' neural networks, where each tower learns one particular stakeholder objective based on the music listening records. These towers are then connected through a novel ``bridge'' architecture between hidden layers of tower neural networks, which bidirectionally share the latent information across the towers to capture the heterogeneous relationships between the different objectives. We further propose a novel Shapley-oriented mechanism to identify the optimal location of the bridge to maximize its information-sharing effectiveness. Finally, we aggregate these predicted objectives through a hyperparameter optimization approach to generate the utility values of candidate music and produce recommendations. We empirically demonstrate the benefits of our proposed framework through extensive experiments on two proprietary industrial-scale datasets provided by Spotify and on one public music listening history dataset from Last.fm, where our model achieves significant performance improvements for both stakeholders (artists and listeners), when compared to state-of-the-art music recommendation models and existing multi-objective multi-stakeholder methods.

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