False-name-proof recommendations in social networks

Published

Conference Paper

Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. We study the problem of finding a recommendation for an uninformed user in a social network by weighting and aggregating the opinions offered by the informed users in the network. In social networks, an informed user may try to manipulate the recommendation by performing a false-name manipulation, wherein the user submits multiple opinions through fake accounts. To that end, we impose a no harm axiom: false-name manipulations by a user should not reduce the weight of other users in the network. We show that this axiom has deep connections to false-name-proofness. While it is impossible to design a mechanism that is best for every network subject to this axiom, we propose an intuitive mechanism LEGIT+, and show that it is uniquely optimized for small networks. Using real-world datasets, we show that our mechanism performs very well compared to two baseline mechanisms in a number of metrics, even on large networks.

Duke Authors

Cited Authors

  • Brill, M; Conitzer, V; Freeman, R; Shah, N

Published Date

  • January 1, 2016

Published In

Start / End Page

  • 332 - 340

Electronic International Standard Serial Number (EISSN)

  • 1558-2914

International Standard Serial Number (ISSN)

  • 1548-8403

International Standard Book Number 13 (ISBN-13)

  • 9781450342391

Citation Source

  • Scopus