Group Decision Support for Leisure Activities Using Voting and Social Networks

Ofrit Lesser, Lihi Naamani-Dery, Meir Kalech, Yuval Elovici

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

A group of people is often required to reach a joint decision and choose a single activity in which they will all participate. Members of such group often interact via online social networks. Group decision making requires knowledge of members’ preferences; however, in many cases the members’ preferences are not fully available. We consider a scenario where preferences are known only partially and present a group decision support framework that determines a single winning item using a voting procedure while minimizing the number of queries for members’ preferences. The framework uses a probabilistic algorithm based on the social similarity among the group members. To evaluate our framework we have built LetsDoIt, a decision support system prototype for leisure actives. We compared several types of groups and prediction methods and reveal that for groups with high internal social similarity, the algorithm reaches a decision using fewer queries, and thus less communication with the users is required. Moreover, the runtime of the algorithms using social based prediction methods is less than half that of the algorithm using rating based prediction methods. These results suggest that incorporating social similarity data, when available, can be of value.

Original languageEnglish
Pages (from-to)473-494
Number of pages22
JournalGroup Decision and Negotiation
Volume26
Issue number3
DOIs
StatePublished - 1 May 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.

Keywords

  • Decision support systems
  • Group recommender
  • Social networks
  • Voting

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