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Intent-oriented diversity in recommender systems

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SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1211 - 1212

https://doi.org/10.1145/2009916.2010124

Published: 24 July 2011 Publication History

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Abstract

Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.

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Cited By

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Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/3664928Online publication date: 21-May-2024
https://doi.org/10.1145/3664928
Bai YZhou YDou ZWen J(2024)Intent-Oriented Dynamic Interest Modeling for Personalized Web SearchACM Transactions on Information Systems10.1145/363981742:4(1-30)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3639817
Rook LZanker MJannach DBoratto LGena CMarras MGermanakos PPopescus E(2024)Are We Losing Interest in Context-Aware Recommender Systems?Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665190(229-230)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665190
Show More Cited By

Index Terms

Intent-oriented diversity in recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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

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Cited By

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Zhao YWang YLiu YCheng XAggarwal CDerr T(2024)Fairness and Diversity in Recommender Systems: A SurveyACM Transactions on Intelligent Systems and Technology10.1145/3664928Online publication date: 21-May-2024
https://doi.org/10.1145/3664928
Bai YZhou YDou ZWen J(2024)Intent-Oriented Dynamic Interest Modeling for Personalized Web SearchACM Transactions on Information Systems10.1145/363981742:4(1-30)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3639817
Rook LZanker MJannach DBoratto LGena CMarras MGermanakos PPopescus E(2024)Are We Losing Interest in Context-Aware Recommender Systems?Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665190(229-230)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665190
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
Satinet CFouss FSaerens MLeleux P(2024)In-processing and post-processing strategies for balancing accuracy and sustainability in product recommendationsElectronic Commerce Research and Applications10.1016/j.elerap.2024.10143367:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.elerap.2024.101433
Yadav NPal SSingh A(2023)Diversified recommendation using implicit content node embedding in heterogeneous information networkMultimedia Tools and Applications10.1007/s11042-023-16135-w83:7(20605-20635)Online publication date: 3-Aug-2023
https://doi.org/10.1007/s11042-023-16135-w
Rafique WHafid AQadir J(2023)Developing smart city services using intent‐aware recommendation systems: A surveyTransactions on Emerging Telecommunications Technologies10.1002/ett.472834:4Online publication date: 12-Jan-2023
https://doi.org/10.1002/ett.4728
Zhang XJiang MChen HChen CZheng J(2022)Cloze-Style Data Augmentation for Few-Shot Intent RecognitionMathematics10.3390/math1018335810:18(3358)Online publication date: 16-Sep-2022
https://doi.org/10.3390/math10183358
Karakolis EKokkinakos PAskounis D(2022)Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender SystemsApplied Sciences10.3390/app1210498412:10(4984)Online publication date: 14-May-2022
https://doi.org/10.3390/app12104984
Porcaro LGómez ECastillo C(2022)Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track CharacteristicsProceedings of the ACM on Human-Computer Interaction10.1145/35129566:CSCW1(1-26)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512956
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