research-article

Temporal diversity in recommender systems

Authors:

Neal Lathia,

Stephen Hailes,

Licia Capra,

Xavier AmatriainAuthors Info & Claims

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 210 - 217

https://doi.org/10.1145/1835449.1835486

Published: 19 July 2010 Publication History

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Abstract

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the system's top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.

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Cited By

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Pandey SDas SGanu HSingh SBalsamo SKnottenbelt WAbad CShang W(2024)Rethinking 'Complement' Recommendations at Scale with SIMDProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645041(25-36)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645041
Wu HZhang YMa CLyu FHe BMitra BLiu X(2024)Result Diversification in Search and Recommendation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338226236:10(5354-5373)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2024.3382262
Zuo PJia Z(2024)A New Recommender Algorithm Based on Signed Networks and Attitude Information Consistency on Online Rating Systems2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE)10.1109/ICAACE61206.2024.10548818(231-235)Online publication date: 1-Mar-2024
https://doi.org/10.1109/ICAACE61206.2024.10548818
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Index Terms

Temporal diversity in recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

July 2010

944 pages

ISBN:9781450301534

DOI:10.1145/1835449

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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SIGIR '10

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SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

Acceptance Rates

SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Pandey SDas SGanu HSingh SBalsamo SKnottenbelt WAbad CShang W(2024)Rethinking 'Complement' Recommendations at Scale with SIMDProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645041(25-36)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645041
Wu HZhang YMa CLyu FHe BMitra BLiu X(2024)Result Diversification in Search and Recommendation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338226236:10(5354-5373)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2024.3382262
Zuo PJia Z(2024)A New Recommender Algorithm Based on Signed Networks and Attitude Information Consistency on Online Rating Systems2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE)10.1109/ICAACE61206.2024.10548818(231-235)Online publication date: 1-Mar-2024
https://doi.org/10.1109/ICAACE61206.2024.10548818
Lesan Sedgh MLatif AEmadi S(2024)A novel method for a technology enhanced learning recommender system considering changing user interest based on neural collaborative filteringData Science and Management10.1016/j.dsm.2024.09.004Online publication date: Oct-2024
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Gu PHu HWang DYu DXu G(2024)Temporal Diversity-Aware Micro-Video Recommendation with Long- and Short-Term Interests ModelingNeural Processing Letters10.1007/s11063-024-11652-756:3Online publication date: 3-Jun-2024
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Zellner LRauch SSontheim JSeidl T(2024)On Diverse and Precise Recommendations for Small and Medium-Sized EnterprisesAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_10(118-130)Online publication date: 25-Apr-2024
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Kar PRoy MDatta SKar PRoy MDatta S(2024)Collaborative Filtering and Content-Based SystemsRecommender Systems: Algorithms and their Applications10.1007/978-981-97-0538-2_3(19-30)Online publication date: 12-Jun-2024
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Miehling ENair RDaly ERamamurthy KRedmond ROh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Cookie consent has disparate impact on estimation accuracyProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667610(34308-34328)Online publication date: 10-Dec-2023
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Shi XLiu QXie HWu DPeng BShang MLian D(2023)Relieving Popularity Bias in Interactive Recommendation: A Diversity-Novelty-Aware Reinforcement Learning ApproachACM Transactions on Information Systems10.1145/361810742:2(1-30)Online publication date: 8-Nov-2023
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Ekstrand MCarterette BDiaz F(2023)Distributionally-Informed Recommender System EvaluationACM Transactions on Recommender Systems10.1145/36134552:1(1-27)Online publication date: 5-Aug-2023
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A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems

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