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Statistical models of music-listening sessions in social media

Published: 26 April 2010 Publication History

Abstract

User experience in social media involves rich interactions with the media content and other participants in the community. In order to support such communities, it is important to understand the factors that drive the users' engagement. In this paper we show how to define statistical models of different complexity to describe patterns of song listening in an online music community. First, we adapt the LDA model to capture music taste from listening activities across users and identify both the groups of songs associated with the specific taste and the groups of listeners who share the same taste. Second, we define a graphical model that takes into account listening sessions and captures the listening moods of users in the community. Our session model leads to groups of songs and groups of listeners with similar behavior across listening sessions and enables faster inference when compared to the LDA model. Our experiments with the data from an online media site demonstrate that the session model is better in terms of the perplexity compared to two other models: the LDA-based taste model that does not incorporate cross-session information and a baseline model that does not use latent groupings of songs.

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Cited By

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  • (2025)Aggregating Contextual Information for Multi-Criteria Online Music RecommendationsIEEE Access10.1109/ACCESS.2025.352751213(8790-8805)Online publication date: 2025
  • (2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
  • (2023)Rethinking Rule-Based Approaches in Session-Based RecommendationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10094851(1-5)Online publication date: 4-Jun-2023
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    cover image ACM Other conferences
    WWW '10: Proceedings of the 19th international conference on World wide web
    April 2010
    1407 pages
    ISBN:9781605587998
    DOI:10.1145/1772690

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 April 2010

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    Author Tags

    1. collaborative filtering
    2. graphical models
    3. mood
    4. music
    5. recommendations
    6. sessions
    7. social media
    8. taste

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    WWW '10
    WWW '10: The 19th International World Wide Web Conference
    April 26 - 30, 2010
    North Carolina, Raleigh, USA

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

    View all
    • (2025)Aggregating Contextual Information for Multi-Criteria Online Music RecommendationsIEEE Access10.1109/ACCESS.2025.352751213(8790-8805)Online publication date: 2025
    • (2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
    • (2023)Rethinking Rule-Based Approaches in Session-Based RecommendationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10094851(1-5)Online publication date: 4-Jun-2023
    • (2022)Modeling Latent Autocorrelation for Session-based RecommendationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557645(4605-4609)Online publication date: 17-Oct-2022
    • (2021)A Survey on Session-based Recommender SystemsACM Computing Surveys10.1145/346540154:7(1-38)Online publication date: 18-Jul-2021
    • (2021)Follow the guides: disentangling human and algorithmic curation in online music consumptionProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474269(380-389)Online publication date: 13-Sep-2021
    • (2021)Sequence-aware similarity learning for next-item recommendationThe Journal of Supercomputing10.1007/s11227-020-03555-wOnline publication date: 4-Jan-2021
    • (2021)Music Recommendation Systems: Techniques, Use Cases, and ChallengesRecommender Systems Handbook10.1007/978-1-0716-2197-4_24(927-971)Online publication date: 22-Nov-2021
    • (2020)The multimedia recommendation algorithm based on probability graphical modelMultimedia Tools and Applications10.1007/s11042-020-10129-881:14(19035-19050)Online publication date: 29-Oct-2020
    • (2020)Recommendation system based on semantic scholar mining and topic modeling on conference publicationsSoft Computing10.1007/s00500-020-05397-3Online publication date: 3-Nov-2020
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