Article

The keepup recommender system

Authors:

Andrew Webster,

Julita VassilevaAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 173 - 176

https://doi.org/10.1145/1297231.1297264

Published: 19 October 2007 Publication History

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Abstract

In this short paper, we describe our RSS recommender system, KeepUP. Too often recommender systems are seen as black box systems, resulting in general perplexity and dissatisfaction from users who are treated as passive, isolated consumers. Recent literature observes that recommendations rarely occur within such isolation and that there may be potential within more socially-orientated approaches. With KeepUP, we outline the design of a recommendation process that is based on an implicit social network where the relevancy and meaning of information can be negotiated not only with the recommender system but also with other users. Our overall goal is to support the formation and development of online communities of interest.

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Cited By

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Nagulendra SVassileva J(2016)Providing awareness, explanation and control of personalized filtering in a social networking siteInformation Systems Frontiers10.1007/s10796-015-9577-y18:1(145-158)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1007/s10796-015-9577-y
Santos OBoticario J(2015)User-centred design and educational data mining support during the recommendations elicitation process in social online learning environmentsExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1204132:2(293-311)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1111/exsy.12041
Nagulendra SVassileva JFerres LRossi GAlmeida VHerder E(2014)Understanding and controlling the filter bubble through interactive visualizationProceedings of the 25th ACM conference on Hypertext and social media10.1145/2631775.2631811(107-115)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1145/2631775.2631811
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Index Terms

The keepup recommender system
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

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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Association for Computing Machinery

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Published: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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

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Nagulendra SVassileva J(2016)Providing awareness, explanation and control of personalized filtering in a social networking siteInformation Systems Frontiers10.1007/s10796-015-9577-y18:1(145-158)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1007/s10796-015-9577-y
Santos OBoticario J(2015)User-centred design and educational data mining support during the recommendations elicitation process in social online learning environmentsExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1204132:2(293-311)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1111/exsy.12041
Nagulendra SVassileva JFerres LRossi GAlmeida VHerder E(2014)Understanding and controlling the filter bubble through interactive visualizationProceedings of the 25th ACM conference on Hypertext and social media10.1145/2631775.2631811(107-115)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1145/2631775.2631811
Nagulendra SVassileva J(2013)Providing Awareness, Understanding and Control of Personalized Stream Filtering in a P2P Social NetworkProceedings of the 19th International Conference on Collaboration and Technology - Volume 822410.1007/978-3-642-41347-6_6(61-76)Online publication date: 30-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-642-41347-6_6
Leite Dantas Bezerra BTenorio De Carvalho F(2011)Symbolic data analysis tools for recommendation systemsKnowledge and Information Systems10.5555/3225647.322586726:3(385-418)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.5555/3225647.3225867
Tandukar UVassileva J(2011)Selective propagation of social data in decentralized online social networkProceedings of the 19th international conference on Advances in User Modeling10.1007/978-3-642-28509-7_20(213-224)Online publication date: 11-Jul-2011
https://dl.acm.org/doi/10.1007/978-3-642-28509-7_20
Leite Dantas Bezerra BTenorio de Carvalho F(2010)Symbolic data analysis tools for recommendation systemsKnowledge and Information Systems10.1007/s10115-009-0282-326:3(385-418)Online publication date: 10-Feb-2010
https://doi.org/10.1007/s10115-009-0282-3
Carmagnola FVernero FGrillo P(2009)SoNARSProceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH10.1007/978-3-642-02247-0_22(223-234)Online publication date: 1-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-02247-0_22
Vassileva J(2008)Toward Social Learning EnvironmentsIEEE Transactions on Learning Technologies10.1109/TLT.2009.41:4(199-214)Online publication date: 1-Oct-2008
https://dl.acm.org/doi/10.1109/TLT.2009.4
Delacroix S(undefined)Beware of 'algorithmic regulation'SSRN Electronic Journal10.2139/ssrn.3327191
https://doi.org/10.2139/ssrn.3327191

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Abstract

References

Cited By

Index Terms

Recommendations

Directed Edge Recommender System

A tag-based recommender system using rule-based collaborative profile enrichment

A semantic recommender system based on frequent tag pattern

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