research-article

Towards Seed-Free Music Playlist Generation: Enhancing Collaborative Filtering with Playlist Title Information

Authors:

Cynthia C. S. Liem,

Alan HanjalicAuthors Info & Claims

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

Article No.: 14, Pages 1 - 6

https://doi.org/10.1145/3267471.3267485

Published: 02 October 2018 Publication History

Abstract

In this paper, we propose a hybrid Neural Collaborative Filtering (NCF) model trained with a multi-objective function to achieve a music playlist generation system. The proposed approach focuses particularly on the cold-start problem (playlists with no seed tracks) and uses a text encoder employing a Recurrent Neural Network (RNN) to exploit textual information given by the playlist title. To accelerate the training, we first apply Weighted Regularized Matrix Factorization (WRMF) as the basic recommendation model to pre-learn latent factors of playlists and tracks. These factors then feed into the proposed multi-objective optimization that also involves embeddings of playlist titles. The experimental study indicates that the proposed approach can effectively suggest suitable music tracks for a given playlist title, compensating poor original recommendation results made on empty playlists by the WRMF model.

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Cited By

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
Sakurai KTogo ROgawa THaseyama M(2022)Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement LearningSensors10.3390/s2210372222:10(3722)Online publication date: 13-May-2022
https://doi.org/10.3390/s22103722
Rohani DSpringer AHollis VBardram JWhittaker S(2021)Recommending Activities for Mental Health and Well-Being: Insights From Two User StudiesIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.29720079:3(1183-1193)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TETC.2020.2972007
Show More Cited By

Index Terms

Towards Seed-Free Music Playlist Generation: Enhancing Collaborative Filtering with Playlist Title Information
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Music retrieval
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Other conferences

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

October 2018

96 pages

ISBN:9781450365864

DOI:10.1145/3267471

Copyright © 2018 ACM.

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 the author(s) 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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Overall Acceptance Rate 11 of 15 submissions, 73%

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Cited By

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
Sakurai KTogo ROgawa THaseyama M(2022)Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement LearningSensors10.3390/s2210372222:10(3722)Online publication date: 13-May-2022
https://doi.org/10.3390/s22103722
Rohani DSpringer AHollis VBardram JWhittaker S(2021)Recommending Activities for Mental Health and Well-Being: Insights From Two User StudiesIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2020.29720079:3(1183-1193)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TETC.2020.2972007
Sassi IYahia SLiiv I(2021)MORec: At the Crossroads of Context-Aware and Multi-Criteria Decision Making for Online Music RecommendationExpert Systems with Applications10.1016/j.eswa.2021.115375(115375)Online publication date: Jun-2021
https://doi.org/10.1016/j.eswa.2021.115375
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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