GroupFun

November 13, 2013
Title: Group recommender systems as a voting problem
Year: 2010-2012
Researchers: George Popescu and Pearl Pu
Keywords: Voting, Recommender systems, Group decision making, Social choice, Social websites, Dynamic selection, Preference aggregation, User incentives, Game Theory, Music preference, online user decision and behavior, social websites
Abstract: Aggregating users’ individual preference and recommending a common set of items for a group has become a challenging topic in group recommender systems and social websites. This issue is mainly concerned with the following three objectives: eliciting individual users’ preferences, suggesting outcomes that maximize the overall satisfaction for all users and ensuring that the aggregation mechanism is resistant to individual users’ manipulation. Firstly we show how our proposed probabilistic weighted-sum algorithm (PWS) works and emphasize on its advantages. Then we compare PWS with related approaches implemented in similar systems using the case of our music recommender, GroupFun. We describe an experiment design to study users’ perceptions of the algorithms, their perceived fairness and incentives to manipulate the final recommendation outcome. We expect our results to show that PWS will be perceived as fair and diversity- and discovery-driven, thus enhancing the group’s satisfaction. Our future work will focus on the actual evaluation of GroupFun using the experiment design presented here.
Sponsor: Swiss National Science Foundation
Links: Main page, Application