tutorial

Learning to rank for recommender systems

Authors:

Alexandros Karatzoglou,

Linas Baltrunas,

Yue ShiAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 493 - 494

https://doi.org/10.1145/2507157.2508063

Published: 12 October 2013 Publication History

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Abstract

Recommender system aim at providing a personalized list of items ranked according to the preferences of the user, as such ranking methods are at the core of many recommendation algorithms. The topic of this tutorial focuses on the cutting-edge algorithmic development in the area of recommender systems. This tutorial will provide an in depth picture of the progress of ranking models in the field, summarizing the strengths and weaknesses of existing methods, and discussing open issues that could be promising for future research in the community. A qualitative and quantitative comparison between different models will be provided while we will also highlight recent developments in the areas of Reinforcement Learning.

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Cited By

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Çiçeklioğlu A(2024)HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARIKritik İletişim Çalışmaları Dergisi10.53281/kritik.14383066:1(99-130)Online publication date: 2-Jul-2024
https://doi.org/10.53281/kritik.1438306
Zhang CChen WZhang WXu M(2024)Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting ItemsACM Transactions on Intelligent Systems and Technology10.1145/3653983Online publication date: 26-Mar-2024
https://doi.org/10.1145/3653983
Luo YChen ZZhou PWu ZGao XJiang Y(2024)Learning to Rank Patches for Unbiased Image Redundancy Reduction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02154(22831-22840)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02154
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Index Terms

Learning to rank for recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2013

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Tutorial

Conference

RecSys '13

Sponsor:

SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

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Çiçeklioğlu A(2024)HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARIKritik İletişim Çalışmaları Dergisi10.53281/kritik.14383066:1(99-130)Online publication date: 2-Jul-2024
https://doi.org/10.53281/kritik.1438306
Zhang CChen WZhang WXu M(2024)Mitigating the Impact of Inaccurate Feedback in Dynamic Learning-to-Rank: A Study of Overlooked Interesting ItemsACM Transactions on Intelligent Systems and Technology10.1145/3653983Online publication date: 26-Mar-2024
https://doi.org/10.1145/3653983
Luo YChen ZZhou PWu ZGao XJiang Y(2024)Learning to Rank Patches for Unbiased Image Redundancy Reduction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02154(22831-22840)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02154
Bałchanowski MBoryczka U(2024)How Normalization Strategies Affect the Quality of Rank Aggregation Methods in Recommendation SystemsProcedia Computer Science10.1016/j.procs.2023.10.174225:C(1843-1852)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.174
Wang CLiu AQin T(2024)Learning-to-rank debias with popularity-weighted negative sampling and popularity regularizationNeurocomputing10.1016/j.neucom.2024.127681587(127681)Online publication date: Jun-2024
https://doi.org/10.1016/j.neucom.2024.127681
Chen XZhu RDing BWang SZhou J(2024)Lero: applying learning-to-rank in query optimizerThe VLDB Journal10.1007/s00778-024-00850-333:5(1307-1331)Online publication date: 25-Apr-2024
https://doi.org/10.1007/s00778-024-00850-3
Jadon APatil A(2024)A Comprehensive Survey of Evaluation Techniques for Recommendation SystemsComputation of Artificial Intelligence and Machine Learning10.1007/978-3-031-71484-9_25(281-304)Online publication date: 25-Sep-2024
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Sagtani HJeunen OUstimenko A(2024)Learning-to-Rank with Nested FeedbackAdvances in Information Retrieval10.1007/978-3-031-56063-7_22(306-315)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56063-7_22
Bałchanowski MBoryczka U(2023)A Comparative Study of Rank Aggregation Methods in Recommendation SystemsEntropy10.3390/e2501013225:1(132)Online publication date: 9-Jan-2023
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Zhu RChen WDing BChen XPfadler AWu ZZhou J(2023)Lero: A Learning-to-Rank Query OptimizerProceedings of the VLDB Endowment10.14778/3583140.358316016:6(1466-1479)Online publication date: 1-Feb-2023
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