research-article

On Achieving Diversity in Recommender Systems

Authors:

Marialena Kyriakidi,

Kostas Stefanidis,

Yannis IoannidisAuthors Info & Claims

ExploreDB'17: Proceedings of the ExploreDB'17

Article No.: 4, Pages 1 - 6

https://doi.org/10.1145/3077331.3077341

Published: 14 May 2017 Publication History

Abstract

Throughout our digital lives, we are getting recommendations for about almost everything we do, buy or consume. In that way, the field of recommender systems has been evolving vastly to match the increasing user needs accordingly. News, products, ideas and people are only a few of the things that we can be recommended with daily. However, even with the many years of research, several areas still remain unexplored. The focus of this paper revolves around such an area, namely on how to achieve diversity in single-user and group recommendations. Specifically, we decouple diversity from strictly revolving around items, and consider it as an orthogonal dimension that can be incorporated independently at different times in the recommender's workflow. We consider various definitions of diversity, taking into account either data items or users characteristics, and study how to cope with them, depending on whether we opt at diversity-aware single-user or group recommendations.

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Cited By

Yadav NPal SSingh ASingh K(2022)Clus-DR: Cluster-based pre-trained model for diverse recommendation generationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.02.01034:8(6385-6399)Online publication date: Sep-2022
https://doi.org/10.1016/j.jksuci.2022.02.010
Nikookar SEsfandiari MBorromeo RSakharkar PAmer-Yahia SBasu Roy S(2022)Diversifying recommendations on sequences of setsThe VLDB Journal10.1007/s00778-022-00740-632:2(283-304)Online publication date: 17-May-2022
https://doi.org/10.1007/s00778-022-00740-6
Koutsopoulos IHalkidi M(2021)Optimization of Multi-stakeholder Recommender Systems for Diversity and CoverageArtificial Intelligence Applications and Innovations10.1007/978-3-030-79150-6_55(703-714)Online publication date: 22-Jun-2021
https://doi.org/10.1007/978-3-030-79150-6_55
Show More Cited By

On Achieving Diversity in Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image ACM Conferences

ExploreDB'17: Proceedings of the ExploreDB'17

May 2017

36 pages

ISBN:9781450346740

DOI:10.1145/3077331

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 May 2017

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SIGMOD/PODS'17

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SIGMOD/PODS'17: International Conference on Management of Data

May 14 - 19, 2017

IL, Chicago, USA

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Overall Acceptance Rate 11 of 21 submissions, 52%

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Cited By

Yadav NPal SSingh ASingh K(2022)Clus-DR: Cluster-based pre-trained model for diverse recommendation generationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.02.01034:8(6385-6399)Online publication date: Sep-2022
https://doi.org/10.1016/j.jksuci.2022.02.010
Nikookar SEsfandiari MBorromeo RSakharkar PAmer-Yahia SBasu Roy S(2022)Diversifying recommendations on sequences of setsThe VLDB Journal10.1007/s00778-022-00740-632:2(283-304)Online publication date: 17-May-2022
https://doi.org/10.1007/s00778-022-00740-6
Koutsopoulos IHalkidi M(2021)Optimization of Multi-stakeholder Recommender Systems for Diversity and CoverageArtificial Intelligence Applications and Innovations10.1007/978-3-030-79150-6_55(703-714)Online publication date: 22-Jun-2021
https://doi.org/10.1007/978-3-030-79150-6_55
Paraschakis DNilsson B(2020)Matchmaking Under Fairness Constraints: A Speed Dating Case StudyBias and Social Aspects in Search and Recommendation10.1007/978-3-030-52485-2_5(43-57)Online publication date: 12-Jul-2020
https://doi.org/10.1007/978-3-030-52485-2_5
Yang YChen HLu WAyala BChen JGonçalves MAllen JFox EKan MPetras V(2018)Diversifying Citation Contexts in Academic Literature for Knowledge RecommendationProceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries10.1145/3197026.3203904(397-398)Online publication date: 23-May-2018
https://dl.acm.org/doi/10.1145/3197026.3203904
Koutsopoulos IHalkidi M(2018)Efficient and Fair Item Coverage in Recommender Systems2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000-9(912-918)Online publication date: Aug-2018
https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000-9
Koskela MSimola IStefanidis K(2018)Open Source Software Recommendations Using GithubDigital Libraries for Open Knowledge10.1007/978-3-030-00066-0_24(279-285)Online publication date: 5-Sep-2018
https://doi.org/10.1007/978-3-030-00066-0_24

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