Group Oriented Trust-aware Location Recommendation for Location-based Social Networks

With the increasing popularity of social networks and online communities, group recommendation systems arise in order to support users to interact with those having similar interests, and to provide recommendations for joint activities, such as eating out as a group or seeing a movie with friends. However, the techniques and approaches to provide recommendations to groups are limited, as most of the available studies focus on individual recommendations. In this study, we address the problem of recommending venues to a group of users by employing Random Walk with Restart (RWR) algorithm to generate recommendations based on the current location of group members, experts and trusted users visiting the same venues. We propose a new approach by including the trust factor of users in location-based social networks (LBSNs). The first one aggregates the location recommendations that are generated with the Random Walk algorithm for each member in the group, taking the preferences and objectivity scores of the individuals into account. The second one is based on creating a group profile by blending preferences and venue category types, and using this group profile to run the Random Walk algorithm once. Comprehensive experiments have been performed on different group sizes, and including trust factor of users. The analysis is conducted on the data collected from the location based social network platform Foursquare. The experiments have shown that the trust factor of users improves the performance of group recommendation system and the proposed algorithm provides recommendations to groups with high accuracy compared to the baselines.