research-article

Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning

Authors:

Kyogu LeeAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 229 - 236

https://doi.org/10.1145/3523227.3546768

Published: 13 September 2022 Publication History

Abstract

Advanced music recommendation systems are being introduced along with the development of machine learning. However, it is essential to design a music recommendation system that can increase user satisfaction by understanding users’ music tastes, not by the complexity of models. Although several studies related to music recommendation systems exploiting negative preferences have shown performance improvements, there was a lack of explanation on how they led to better recommendations. In this work, we analyze the role of negative preference in users’ music tastes by comparing music recommendation models with contrastive learning exploiting preference (CLEP) but with three different training strategies - exploiting preferences of both positive and negative (CLEP-PN), positive only (CLEP-P), and negative only (CLEP-N). We evaluate the effectiveness of the negative preference by validating each system with a small amount of personalized data obtained via survey and further illuminate the possibility of exploiting negative preference in music recommendations. Our experimental results show that CLEP-N outperforms the other two in accuracy and false positive rate. Furthermore, the proposed training strategies produced a consistent tendency regardless of different types of front-end musical feature extractors, proving the stability of the proposed method.

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Cited By

Seshadri PShashaani SKnees P(2024)Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688188(1028-1032)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688188
Tamm YAljanaki A(2024)Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688172(934-938)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688172
Yi ZOunis I(2024)A Unified Graph Transformer for Overcoming Isolations in Multi-modal RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688096(518-527)Online publication date: 8-Oct-2024
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Index Terms

Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 ACM.

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Published: 13 September 2022

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Cited By

Seshadri PShashaani SKnees P(2024)Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688188(1028-1032)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688188
Tamm YAljanaki A(2024)Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688172(934-938)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688172
Yi ZOunis I(2024)A Unified Graph Transformer for Overcoming Isolations in Multi-modal RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688096(518-527)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688096
Chen SChen JZhou SWang BHan SSu CYuan YWang CHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)SIGformer: Sign-aware Graph Transformer for RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657747(1274-1284)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657747
Wang YZhang DWulamu A(2024)A Multi-User-Multi-Scenario-Multi-Mode aware network for personalized recommender systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108169133(108169)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108169
Deldjoo YSchedl MKnees P(2024)Content-driven music recommendationComputer Science Review10.1016/j.cosrev.2024.10061851:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.cosrev.2024.100618
Hu HLiu QLi CKan M(2024)Lightweight Modality Adaptation to Sequential Recommendation via Correlation SupervisionAdvances in Information Retrieval10.1007/978-3-031-56027-9_8(123-139)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56027-9_8
Said AWillemsen MMarinho LSilva I(2023)Leveraging Large Language Models for Goal-driven Interactive RecommendationsProceedings of the 11th International Conference on Human-Agent Interaction10.1145/3623809.3623965(464-466)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3623809.3623965
Wang YHalpern YChang SFeng JLe ELi LLiang XHuang MLi SBeutel AZhang YBi S(2023)Learning from Negative User Feedback and Measuring Responsiveness for Sequential RecommendersProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610244(1049-1053)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610244
Spillo G(2023)Knowledge-Aware Recommender Systems based on Multi-Modal Information SourcesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608866(1312-1317)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608866
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