Aggregating Contextual Information for Multi-Criteria Online Music Recommendations
This paper introduces CAMCMusic, a novel context-aware multi-criteria music recommendation system designed to address these limitations without relying on user-specific attributes, music features, or explicit user ratings. CAMCMusic integrates contextual information into a multi-criteria decision-ma...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10835098/ |
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| author | Jieqi Liu |
| author_facet | Jieqi Liu |
| author_sort | Jieqi Liu |
| collection | DOAJ |
| description | This paper introduces CAMCMusic, a novel context-aware multi-criteria music recommendation system designed to address these limitations without relying on user-specific attributes, music features, or explicit user ratings. CAMCMusic integrates contextual information into a multi-criteria decision-making framework to align music genre recommendations with the user’s contextual situation for generating Top-N music recommendations. CAMCMusic begins by assessing the relevance of contextual information based on the relationship between three key elements: the user, the music genre, and the user’s context. Subsequently, we employ an aggregation technique to reveal the connection between context and overall ratings. On the MusiClef dataset, CAMCMusic achieves a precision@5 of 0.61, a recall@5 of 0.77, and F1@5 of 0.68, surpassing the next best performer, SVD++, by 52.5%, 11.6%, and 36%, respectively. On the CAL500 dataset, CAMCMusic outperforms the FM algorithm by 11.9% in precision, 16.4% in recall, and 20% in F1-Score. CAMCMusic significantly enhances recommendation outcomes by effectively capturing and utilizing the relationships between user contexts and music genres, thus providing a robust solution to the cold start problem while maintaining high recommendation quality and user satisfaction. The findings underscore the potential of combining context-awareness with multi-criteria decision-making (MCDM) to advance the state of the art in music recommendation systems. |
| format | Article |
| id | doaj-art-d92f159520e7425584534665aa684a0d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d92f159520e7425584534665aa684a0d2025-01-21T00:01:51ZengIEEEIEEE Access2169-35362025-01-01138790880510.1109/ACCESS.2025.352751210835098Aggregating Contextual Information for Multi-Criteria Online Music RecommendationsJieqi Liu0https://orcid.org/0009-0006-6230-6699Department of Music, Taiyuan Normal University, Taiyuan, ChinaThis paper introduces CAMCMusic, a novel context-aware multi-criteria music recommendation system designed to address these limitations without relying on user-specific attributes, music features, or explicit user ratings. CAMCMusic integrates contextual information into a multi-criteria decision-making framework to align music genre recommendations with the user’s contextual situation for generating Top-N music recommendations. CAMCMusic begins by assessing the relevance of contextual information based on the relationship between three key elements: the user, the music genre, and the user’s context. Subsequently, we employ an aggregation technique to reveal the connection between context and overall ratings. On the MusiClef dataset, CAMCMusic achieves a precision@5 of 0.61, a recall@5 of 0.77, and F1@5 of 0.68, surpassing the next best performer, SVD++, by 52.5%, 11.6%, and 36%, respectively. On the CAL500 dataset, CAMCMusic outperforms the FM algorithm by 11.9% in precision, 16.4% in recall, and 20% in F1-Score. CAMCMusic significantly enhances recommendation outcomes by effectively capturing and utilizing the relationships between user contexts and music genres, thus providing a robust solution to the cold start problem while maintaining high recommendation quality and user satisfaction. The findings underscore the potential of combining context-awareness with multi-criteria decision-making (MCDM) to advance the state of the art in music recommendation systems.https://ieeexplore.ieee.org/document/10835098/Recommender systemsmusicuser-centric computingmulti-criteria decisioncontext-aware recommendation systemplaylist continuation |
| spellingShingle | Jieqi Liu Aggregating Contextual Information for Multi-Criteria Online Music Recommendations IEEE Access Recommender systems music user-centric computing multi-criteria decision context-aware recommendation system playlist continuation |
| title | Aggregating Contextual Information for Multi-Criteria Online Music Recommendations |
| title_full | Aggregating Contextual Information for Multi-Criteria Online Music Recommendations |
| title_fullStr | Aggregating Contextual Information for Multi-Criteria Online Music Recommendations |
| title_full_unstemmed | Aggregating Contextual Information for Multi-Criteria Online Music Recommendations |
| title_short | Aggregating Contextual Information for Multi-Criteria Online Music Recommendations |
| title_sort | aggregating contextual information for multi criteria online music recommendations |
| topic | Recommender systems music user-centric computing multi-criteria decision context-aware recommendation system playlist continuation |
| url | https://ieeexplore.ieee.org/document/10835098/ |
| work_keys_str_mv | AT jieqiliu aggregatingcontextualinformationformulticriteriaonlinemusicrecommendations |