Article

A Hybrid Recommendation for Music Based on Reinforcement Learning

Author:

Yu WangAuthors Info & Claims

Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part I

Pages 91 - 103

https://doi.org/10.1007/978-3-030-47426-3_8

Published: 11 May 2020 Publication History

Abstract

The key to personalized recommendation system is the prediction of users’ preferences. However, almost all existing music recommendation approaches only learn listeners’ preferences based on their historical records or explicit feedback, without considering the simulation of interaction process which can capture the minor changes of listeners’ preferences sensitively. In this paper, we propose a personalized hybrid recommendation algorithm for music based on reinforcement learning (PHRR) to recommend song sequences that match listeners’ preferences better. We firstly use weighted matrix factorization (WMF) and convolutional neural network (CNN) to learn and extract the song feature vectors. In order to capture the changes of listeners’ preferences sensitively, we innovatively enhance simulating interaction process of listeners and update the model continuously based on their preferences both for songs and song transitions. The extensive experiments on real-world datasets validate the effectiveness of the proposed PHRR on song sequence recommendation compared with the state-of-the-art recommendation approaches.

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Cited By

Wang SChen XYao LSerra ESpezzano F(2024)On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679674(2390-2399)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679674
Cavenaghi ESottocornola GStella FZanker M(2023)A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation SystemsACM Transactions on Recommender Systems10.1145/35965191:3(1-23)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3596519
Zhang TLiu S(2022)Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge GraphSecurity and Communication Networks10.1155/2022/58897242022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5889724

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A Hybrid Recommendation for Music Based on Reinforcement Learning

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cover image Guide Proceedings

Advances in Knowledge Discovery and Data Mining: 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11–14, 2020, Proceedings, Part I

May 2020

905 pages

ISBN:978-3-030-47425-6

DOI:10.1007/978-3-030-47426-3

Editors:
Hady W. Lauw
School of Information Systems, Singapore Management University, Singapore, Singapore
,
Raymond Chi-Wing Wong
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
,
Alexandros Ntoulas
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
,
Ee-Peng Lim
School of Information Systems, Singapore Management University, Singapore, Singapore
,
See-Kiong Ng
Institute of Data Science, National University of Singapore, Singapore, Singapore
,
Sinno Jialin Pan
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 11 May 2020

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Cited By

Wang SChen XYao LSerra ESpezzano F(2024)On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679674(2390-2399)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679674
Cavenaghi ESottocornola GStella FZanker M(2023)A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation SystemsACM Transactions on Recommender Systems10.1145/35965191:3(1-23)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3596519
Zhang TLiu S(2022)Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge GraphSecurity and Communication Networks10.1155/2022/58897242022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5889724

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