Manipulating recommendation lists by global considerations

Alon Grubshtein, Nurit Gal-Oz, Tal Grinshpoun, Amnon Meisels, Roie Zivan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

The designers of trust and reputation systems attempt to provide a rich setting for interacting users. While most research is focused on the validity of recommendations in such settings, we study means of introducing system requirements and secondary goals which we term Global Considerations. Recommendation systems are assumed to be based on a framework which includes two types of entities: service providers and users seeking services (e.g. eBay (eba, )). The present paper formulates a basis for manipulation of information in a manner which does not harm either. These manipulations must be carefully devised: an administrator attempting to manipulate ratings, even for the benefit of most participants, may dampen the gain of service providers, users or both. On the other hand, such changes may produce a more efficient and user friendly system, allow for the improved initialization of new service providers or upgrade existing features. The present paper formulates threshold values which define the limits of our manipulation, propose different concepts for manipulation and evaluates by simulation the performance of systems which employ our manipulations.

Original languageEnglish
Title of host publicationICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Pages135-142
Number of pages8
StatePublished - 13 Sep 2010
Event2nd International Conference on Agents and Artificial Intelligence, ICAART 2010 - Valencia, Spain
Duration: 22 Jan 201024 Jan 2010

Publication series

NameICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence, Proceedings
Volume2

Conference

Conference2nd International Conference on Agents and Artificial Intelligence, ICAART 2010
Country/TerritorySpain
CityValencia
Period22/01/1024/01/10

Keywords

  • Rating manipulations
  • Recommender systems

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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