Handle URI:
http://hdl.handle.net/10754/597532
Title:
An incentive-based architecture for social recommendations
Authors:
Bhattacharjee, Rajat; Goel, Ashish; Kollias, Konstantinos
Abstract:
We present an incentive-based architecture for providing recommendations in a social network. We maintain a distinct reputation system for each individual and we rely on users to identify appropriate correlations and rate the items using a system-provided recommendation language. The key idea is to design an incentive structure and a ranking system such that any inaccuracy in the recommendations implies the existence of a profitable arbitrage opportunity, hence making the system resistant to malicious spam and presentation bias. We also show that, under mild assumptions, our architecture provides users with incentive to minimize the Kullback-Leibler divergence between the ratings and the actual item qualities, quickly driving the system to an equilibrium state with accurate recommendations. Copyright 2009 ACM.
Citation:
Bhattacharjee R, Goel A, Kollias K (2009) An incentive-based architecture for social recommendations. Proceedings of the third ACM conference on Recommender systems - RecSys ’09. Available: http://dx.doi.org/10.1145/1639714.1639755.
Publisher:
Association for Computing Machinery (ACM)
Journal:
Proceedings of the third ACM conference on Recommender systems - RecSys '09
Issue Date:
2009
DOI:
10.1145/1639714.1639755
Type:
Conference Paper
Sponsors:
Research conducted while at Stanford University. Researchsupported by NSF ITR grant 0428868 and NSF award0339262.Department of Management Science and Engineering and(by courtesy) Computer Science, Stanford University. Researchsupported by NSF ITR grant 0428868 and gifts fromGoogle, Microsoft, and Cisco.Department of Management Science and Engineering,Stanford University. Research supported by an A. G. LeventisFoundation Scholarship and the Stanford-KAUST alliancefor excellence in academics.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorBhattacharjee, Rajaten
dc.contributor.authorGoel, Ashishen
dc.contributor.authorKollias, Konstantinosen
dc.date.accessioned2016-02-25T12:41:33Zen
dc.date.available2016-02-25T12:41:33Zen
dc.date.issued2009en
dc.identifier.citationBhattacharjee R, Goel A, Kollias K (2009) An incentive-based architecture for social recommendations. Proceedings of the third ACM conference on Recommender systems - RecSys ’09. Available: http://dx.doi.org/10.1145/1639714.1639755.en
dc.identifier.doi10.1145/1639714.1639755en
dc.identifier.urihttp://hdl.handle.net/10754/597532en
dc.description.abstractWe present an incentive-based architecture for providing recommendations in a social network. We maintain a distinct reputation system for each individual and we rely on users to identify appropriate correlations and rate the items using a system-provided recommendation language. The key idea is to design an incentive structure and a ranking system such that any inaccuracy in the recommendations implies the existence of a profitable arbitrage opportunity, hence making the system resistant to malicious spam and presentation bias. We also show that, under mild assumptions, our architecture provides users with incentive to minimize the Kullback-Leibler divergence between the ratings and the actual item qualities, quickly driving the system to an equilibrium state with accurate recommendations. Copyright 2009 ACM.en
dc.description.sponsorshipResearch conducted while at Stanford University. Researchsupported by NSF ITR grant 0428868 and NSF award0339262.Department of Management Science and Engineering and(by courtesy) Computer Science, Stanford University. Researchsupported by NSF ITR grant 0428868 and gifts fromGoogle, Microsoft, and Cisco.Department of Management Science and Engineering,Stanford University. Research supported by an A. G. LeventisFoundation Scholarship and the Stanford-KAUST alliancefor excellence in academics.en
dc.publisherAssociation for Computing Machinery (ACM)en
dc.subjectIncentivesen
dc.subjectInformation aggregationen
dc.subjectRecommender systemsen
dc.titleAn incentive-based architecture for social recommendationsen
dc.typeConference Paperen
dc.identifier.journalProceedings of the third ACM conference on Recommender systems - RecSys '09en
dc.contributor.institutionGoogle Inc., Mountain View, United Statesen
dc.contributor.institutionStanford University, Palo Alto, United Statesen
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