Back to Search
Start Over
Deep Multi-Objective Multi-Stakeholder Music Recommendation
- Source :
- SSRN Electronic Journal.
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 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.
- Subjects :
- History
Focus (computing)
Polymers and Plastics
Artificial neural network
Computer science
media_common.quotation_subject
Stakeholder
Recommender system
Data science
Industrial and Manufacturing Engineering
Product (category theory)
Business and International Management
Architecture
Transfer of learning
Function (engineering)
media_common
Subjects
Details
- ISSN :
- 15565068
- Database :
- OpenAIRE
- Journal :
- SSRN Electronic Journal
- Accession number :
- edsair.doi...........6209d66c63a70335b48f943df022e06f
- Full Text :
- https://doi.org/10.2139/ssrn.3848670