Adaptive Voting Algorithms for Group and Social Recommender Systems

Abstract

Nowadays online group activities are emerging, as individuals share their preferences, collaborate, discover and interact with their friends and family. Group recommender systems (GRS) use various social resources to make recommendations of items or activities that users are most likely to consume or agree upon. Thus, aggregating preference and recommending a common set of items for a group has become a challenging topic in online systems providing group suggestions and social websites. This issue is mainly concerned with the following three subjects: eliciting individual users’ preferences, suggesting the maximized overall satisfaction outcome for all users and ensuring that the aggregation mechanism is resistant to individual users’ manipulation. Furthermore, both individual and group preferences change over time. In order to track all of these changes GRS need to benefit from user interaction. This paper aims to present an innovative algorithm, which adapts to individual preference dynamics for group and social recommender systems. Individuals choose their desired items with the purpose of maximizing the entire group’s satisfaction.