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Workshop on novelty and diversity in recommender systems - DiveRS 2011

Authors:

Pablo Castells,

Jun Wang,

Rubén Lara,

Dell ZhangAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 393 - 394

https://doi.org/10.1145/2043932.2044019

Published: 23 October 2011 Publication History

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Abstract

Novelty and diversity have been identified as key dimensions of recommendation utility in real scenarios, and a fundamental research direction to keep making progress in the field. Yet recommendation novelty and diversity remain a largely open area for research. The DiveRS workshop gathered researchers and practitioners interested in the role of these dimensions in recommender systems. The workshop seeks to advance towards a better understanding of what novelty and diversity are, how they can improve the effectiveness of recommendation methods and the utility of their outputs. The workshop pursued the identification of open problems, relevant research directions, and opportunities for innovation in the recommendation business.

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Wang XHan LLi JYan H(2024)A conditional random field recommendation method based on tripartite graphExpert Systems with Applications10.1016/j.eswa.2023.121804238(121804)Online publication date: Mar-2024
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Noel JMonterola CTan D(2024)Improving recommendation diversity without retraining from scratchInternational Journal of Data Science and Analytics10.1007/s41060-024-00518-9Online publication date: 10-Mar-2024
https://doi.org/10.1007/s41060-024-00518-9
Zickler ENicola SBettencourt N(2023)A Recommender System to Close Skill Gaps and Drive Organisations’ SuccessInnovations in Bio-Inspired Computing and Applications10.1007/978-3-031-27499-2_74(806-815)Online publication date: 28-Mar-2023
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Index Terms

Workshop on novelty and diversity in recommender systems - DiveRS 2011
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 '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2011

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Conference

RecSys '11

Sponsor:

SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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

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Cited By

View all

Wang XHan LLi JYan H(2024)A conditional random field recommendation method based on tripartite graphExpert Systems with Applications10.1016/j.eswa.2023.121804238(121804)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121804
Noel JMonterola CTan D(2024)Improving recommendation diversity without retraining from scratchInternational Journal of Data Science and Analytics10.1007/s41060-024-00518-9Online publication date: 10-Mar-2024
https://doi.org/10.1007/s41060-024-00518-9
Zickler ENicola SBettencourt N(2023)A Recommender System to Close Skill Gaps and Drive Organisations’ SuccessInnovations in Bio-Inspired Computing and Applications10.1007/978-3-031-27499-2_74(806-815)Online publication date: 28-Mar-2023
https://doi.org/10.1007/978-3-031-27499-2_74
Dean SRich SRecht BHildebrandt MCastillo CCelis ERuggieri STaylor LZanfir-Fortuna G(2020)Recommendations and user agencyProceedings of the 2020 Conference on Fairness, Accountability, and Transparency10.1145/3351095.3372866(436-445)Online publication date: 22-Jan-2020
https://doi.org/10.1145/3351095.3372866
Shrestha PZhang MLiu YMa S(2020)Curiosity-inspired Personalized Recommendation2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00010(33-40)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00010
Kovalenko VTintarev NPasynkov EBird CBacchelli A(2020)Does Reviewer Recommendation Help Developers?IEEE Transactions on Software Engineering10.1109/TSE.2018.286836746:7(710-731)Online publication date: 1-Jul-2020
https://doi.org/10.1109/TSE.2018.2868367
Liang YBogers TSaid ABrusilovsky PTikk D(2019)Recommender system for developing new preferences and goalsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347054(611-615)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347054
Ferraro ABogers TSaid ABrusilovsky PTikk D(2019)Music cold-start and long-tail recommendationProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347052(586-590)Online publication date: 10-Sep-2019
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Aytekin TKarakaya M(2018)Clustering-based diversity improvement in top-N recommendationJournal of Intelligent Information Systems10.1007/s10844-013-0252-942:1(1-18)Online publication date: 28-Dec-2018
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Raja KPushpa S(2018)Novelty-driven recommendation by using integrated matrix factorization and temporal-aware clustering optimizationInternational Journal of Communication Systems10.1002/dac.385133:13(e3851)Online publication date: 20-Nov-2018
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