research-article

Efficient Similarity Based Methods For The Playlist Continuation Task

Authors:

Guglielmo Faggioli,

Mirko Polato,

Fabio AiolliAuthors Info & Claims

RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

Article No.: 15, Pages 1 - 6

https://doi.org/10.1145/3267471.3267486

Published: 02 October 2018 Publication History

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Abstract

In this paper, the pipeline we used in the RecSys challenge 2018 is reported. We present content-based and collaborative filtering approaches for the definition of the similarity matrices for top-500 recommendation task. In particular, the task consisted in recommending songs to add to partial playlists. Different methods have been proposed depending on the number of available songs in a playlist. We show how an hybrid approach which exploits both content-based and collaborative filtering is effective in this task. Specifically, information derived by the playlist titles helped to tackle the cold-start issue.

References

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Cited By

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Gabbolini GBridge D(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
https://dl.acm.org/doi/10.1145/3688398
Yürekli AKaleli CBilge A(2021)Alleviating the cold-start playlist continuation in music recommendation using latent semantic indexingInternational Journal of Multimedia Information Retrieval10.1007/s13735-021-00214-510:3(185-198)Online publication date: 3-Sep-2021
https://doi.org/10.1007/s13735-021-00214-5
Yürekli ABilge AKaleli C(2021)Exploring playlist titles for cold-start music recommendation: an effectiveness analysisJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02777-312:11(10125-10144)Online publication date: 3-Jan-2021
https://doi.org/10.1007/s12652-020-02777-3
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Index Terms

Efficient Similarity Based Methods For The Playlist Continuation Task
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

October 2018

96 pages

ISBN:9781450365864

DOI:10.1145/3267471

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 October 2018

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RecSys Challenge '18

RecSys Challenge '18: Proceedings of the ACM Recommender Systems Challenge 2018

October 2, 2018

BC, Vancouver, Canada

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Cited By

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Gabbolini GBridge D(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
https://dl.acm.org/doi/10.1145/3688398
Yürekli AKaleli CBilge A(2021)Alleviating the cold-start playlist continuation in music recommendation using latent semantic indexingInternational Journal of Multimedia Information Retrieval10.1007/s13735-021-00214-510:3(185-198)Online publication date: 3-Sep-2021
https://doi.org/10.1007/s13735-021-00214-5
Yürekli ABilge AKaleli C(2021)Exploring playlist titles for cold-start music recommendation: an effectiveness analysisJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02777-312:11(10125-10144)Online publication date: 3-Jan-2021
https://doi.org/10.1007/s12652-020-02777-3
Zamani HSchedl MLamere PChen C(2019)An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist ContinuationACM Transactions on Intelligent Systems and Technology10.1145/334425710:5(1-21)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1145/3344257

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A hybrid approach of topic model and matrix factorization based on two-step recommendation framework