RecLand

Abstract

Social networks have become an important information source. Due to their unprecedented success, these systems have to face an exponentially increasing amount of user generated content. As a consequence, finding relevant users or data matching specific interests is a challenging. We present RecLand, a recommender system that takes advantage of the social graph topology and of the existing contextual information to recommend users. The graphical interface of RecLand shows recommendations that match the topical interests of users and allows to tune the parameters to adapt the recommendations to their needs.