A Deep Multimodal Approach for Cold-start Music Recommendation-Reference-Cited by-同舟云学术

A Deep Multimodal Approach for Cold-start Music Recommendation

Published:2017-08-27 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems
language:
Short-container-title:

Author:

Oramas Sergio¹,Nieto Oriol²,Sordo Mohamed²,Serra Xavier¹

Affiliation:

1. Music Technology Group, Universitat Pompeu Fabra

2. Pandora Media, Inc., Oakland, CA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3125486.3125492

Reference37 articles.

1. Trapit Bansal David Belanger and Andrew McCallum. 2016. Ask the GRU: Multi-task Learning for Deep Text Recommendations. RecSys (2016) 107--114. arXiv:1609.02116 10.1145/2959100.2959180 Trapit Bansal David Belanger and Andrew McCallum. 2016. Ask the GRU: Multi-task Learning for Deep Text Recommendations. RecSys (2016) 107--114. arXiv:1609.02116 10.1145/2959100.2959180

Cited by 55 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid music recommendation with graph neural networks;User Modeling and User-Adapted Interaction;2024-08-24

2. Integrating sentiment features in factorization machines: Experiments on music recommender systems;Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-22

3. Formalizing Multimedia Recommendation through Multimodal Deep Learning;ACM Transactions on Recommender Systems;2024-04-29

4. Content-driven music recommendation: Evolution, state of the art, and challenges;Computer Science Review;2024-02

5. Attributes Relevance in Content-Based Music Recommendation System;Applied Sciences;2024-01-19