Group Finder: An Item-Driven Group Formation Framework

Abstract

Several among our daily activities, like traveling to a tourist attraction, are better enjoyed with a group of friends. However, finding the best travel companions is sometimes tricky since we need to form a group of people combining the interest in the proposed destination with the friendship relations among the group members. In this paper we cope with this problem by proposing a new method to recommend the best group of friends with whom to enjoy a specific item, i.e., a travel destination or a venue to visit. Our approach provides a new and original perspective on recommendation: given a user, her social network and a recommended item that is relevant for the user, we want to suggest the best group of friends with whom enjoying the item. This approach differs from traditional group recommendation since it tries to maximize two orthogonal aspects: i) the relevance of the recommended item for every member of the group, and ii), the intra-group social relationships. We introduce the Group Finder framework defining the User-Item Group Formation problem and the possible solutions. We assess our approach in the domain of location recommendation and experiment the proposed solutions using four different publicly available Location Based Social Network (LBSN) datasets. The results achieved confirm the effectiveness and the feasibility of the proposed solutions that outperform strong baselines.