research-article

Local implicit feedback mining for music recommendation

Authors:

Yong YuAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 91 - 98

https://doi.org/10.1145/2365952.2365973

Published: 09 September 2012 Publication History

Abstract

Digital music has experienced a quite fascinating transformation during the past decades. Thousands of people share or distribute their music collections on the Internet, resulting in an explosive increase of information and more user dependence on automatic recommender systems. Though there are many techniques such as collaborative filtering, most approaches focus mainly on users' global behaviors, neglecting local actions and the specific properties of music. In this paper, we propose a simple and effective local implicit feedback model mining users' local preferences to get better recommendation performance in both rating and ranking prediction. Moreover, we design an efficient training algorithm to speed up the updating procedure, and give a method to find the most appropriate time granularity to assist the performance. We conduct various experiments to evaluate the performance of this model, which show that it outperforms baseline model significantly. Integration with existing temporal models achieves a great improvement compared to the reported best single model for Yahoo! Music.

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Cited By

Qin JRen KFang YZhang WYu YCaverlee JHu XLalmas MWang W(2020)Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation ModelingProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371842(465-473)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371842
Ramírez JFlores M(2020)Machine learning for music genre: multifaceted review and experimentation with audiosetJournal of Intelligent Information Systems10.1007/s10844-019-00582-955:3(469-499)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10844-019-00582-9
Zhang CLiu XBis D(2019)An Analysis on the Learning Rules of the Skip-Gram Model2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852182(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852182
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Index Terms

Local implicit feedback mining for music recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

Copyright © 2012 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 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: 09 September 2012

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RecSys '12

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SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Qin JRen KFang YZhang WYu YCaverlee JHu XLalmas MWang W(2020)Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation ModelingProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371842(465-473)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371842
Ramírez JFlores M(2020)Machine learning for music genre: multifaceted review and experimentation with audiosetJournal of Intelligent Information Systems10.1007/s10844-019-00582-955:3(469-499)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10844-019-00582-9
Zhang CLiu XBis D(2019)An Analysis on the Learning Rules of the Skip-Gram Model2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852182(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852182
Zhang JGao MYu JWang XSong YXiong Q(2019)Nonlinear Transformation for Multiple Auxiliary Information in Music Recommendation2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851992(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851992
Zhang YWang ZDu J(2019)Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851942(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851942
Zhang QRao LYang Y(2019)DGFFM: Generalized Field-aware Factorization Machine based on DenseNet2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851933(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8851933
Cao BHou CPeng HFan JYang JYin JDeng S(2019)Predicting e-book ranking based on the implicit user feedbackWorld Wide Web10.1007/s11280-018-0554-522:2(637-655)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11280-018-0554-5
Wu YXing XChai QJia Z(2018)FitCF: Collaborative Filtering Recommendation Algorithm Based on Nonlinear Fitting Weight Distribution2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)10.1109/DDCLS.2018.8516069(326-331)Online publication date: May-2018
https://doi.org/10.1109/DDCLS.2018.8516069
Sun QTang T(2018)The Design of a Mood-Driven Chinese Song Recommendation System: Combining Valence-Based and Polarity-Based Sentiment Analysis on LyricsIntelligent Systems and Applications10.1007/978-3-030-01057-7_51(669-678)Online publication date: 8-Nov-2018
https://doi.org/10.1007/978-3-030-01057-7_51
Epure EKille BIngvaldsen JDeneckere RSalinesi CAlbayrak SCremonesi PRicci FBerkovsky STuzhilin A(2017)Recommending Personalized News in Short User SessionsProceedings of the Eleventh ACM Conference on Recommender Systems10.1145/3109859.3109894(121-129)Online publication date: 27-Aug-2017
https://dl.acm.org/doi/10.1145/3109859.3109894
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