research-article

Collaborative Ranking with a Push at the Top

Authors:

Konstantina Christakopoulou,

Arindam BanerjeeAuthors Info & Claims

WWW '15: Proceedings of the 24th International Conference on World Wide Web

Pages 205 - 215

https://doi.org/10.1145/2736277.2741678

Published: 18 May 2015 Publication History

Abstract

The goal of collaborative filtering is to get accurate recommendations at the top of the list for a set of users. From such a perspective, collaborative ranking based formulations with suitable ranking loss functions are natural. While recent literature has explored the idea based on objective functions such as NDCG or Average Precision, such objectives are difficult to optimize directly. In this paper, building on recent advances from the learning to rank literature, we introduce a novel family of collaborative ranking algorithms which focus on accuracy at the top of the list for each user while learning the ranking functions collaboratively. We consider three specific formulations, based on collaborative p-norm push, infinite push, and reverse-height push, and propose efficient optimization methods for learning these models. Experimental results illustrate the value of collaborative ranking, and show that the proposed methods are competitive, usually better than existing popular approaches to personalized recommendation.

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Cited By

Marcuzzo MZangari AAlbarelli AGasparetto A(2022)Recommendation Systems: An Insight Into Current Development and Future Research ChallengesIEEE Access10.1109/ACCESS.2022.319453610(86578-86623)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3194536
Jiang RFeng SZhang SLi XYao YZhang H(2022)A personalized recommendation method based on collaborative ranking with random walkMultimedia Tools and Applications10.1007/s11042-022-11980-7Online publication date: 26-Jan-2022
https://doi.org/10.1007/s11042-022-11980-7
Togashi RKato MOtani MSatoh S(2021)Density-Ratio Based Personalised Ranking from Implicit FeedbackProceedings of the Web Conference 202110.1145/3442381.3450027(3221-3233)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450027
Show More Cited By

Index Terms

Collaborative Ranking with a Push at the Top
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

WWW '15: Proceedings of the 24th International Conference on World Wide Web

May 2015

1460 pages

ISBN:9781450334693

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 18 May 2015

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NASA
NSF

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;

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

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Cited By

Marcuzzo MZangari AAlbarelli AGasparetto A(2022)Recommendation Systems: An Insight Into Current Development and Future Research ChallengesIEEE Access10.1109/ACCESS.2022.319453610(86578-86623)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3194536
Jiang RFeng SZhang SLi XYao YZhang H(2022)A personalized recommendation method based on collaborative ranking with random walkMultimedia Tools and Applications10.1007/s11042-022-11980-7Online publication date: 26-Jan-2022
https://doi.org/10.1007/s11042-022-11980-7
Togashi RKato MOtani MSatoh S(2021)Density-Ratio Based Personalised Ranking from Implicit FeedbackProceedings of the Web Conference 202110.1145/3442381.3450027(3221-3233)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450027
Togashi RKato MOtani MSakai TSatoh SDiaz FShah CSuel TCastells PJones RSakai T(2021)Scalable Personalised Item Ranking through Parametric Density EstimationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462933(921-931)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462933
Pal BJenamani MVarma VKambhampati SBhattacharya ANatarajan SRoy R(2020)Personalized Ranking in Collaborative FilteringProceedings of the 7th ACM IKDD CoDS and 25th COMAD10.1145/3371158.3371189(214-218)Online publication date: 5-Jan-2020
https://dl.acm.org/doi/10.1145/3371158.3371189
Aliannejadi MRafailidis DCrestani F(2020)A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.290346332:6(1050-1063)Online publication date: 1-Jun-2020
https://doi.org/10.1109/TKDE.2019.2903463
Li XHan DHe JLiao LWang M(2019)Next and Next New POI Recommendation via Latent Behavior Pattern InferenceACM Transactions on Information Systems10.1145/335418737:4(1-28)Online publication date: 19-Sep-2019
https://dl.acm.org/doi/10.1145/3354187
Rafailidis D(2019)Bayesian Deep Learning with Trust and Distrust in Recommendation SystemsIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352496(18-25)Online publication date: 14-Oct-2019
https://dl.acm.org/doi/10.1145/3350546.3352496
Costa-Jussà MCasas NEscolano CFonollosa J(2019)Chinese-CatalanACM Transactions on Asian and Low-Resource Language Information Processing10.1145/331257518:4(1-8)Online publication date: 22-Apr-2019
https://dl.acm.org/doi/10.1145/3312575
Clear TBeecham S(2019)Global Software Engineering Education Practice Continuum Special Issue of the ACM Transactions on Computing EducationACM Transactions on Computing Education10.1145/329401119:2(1-8)Online publication date: 24-Jan-2019
https://dl.acm.org/doi/10.1145/3294011
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