A triadic closure and homophily-based recommendation system for online social networks

Recommendation systems are popular both commercially and in the research community. For example, Online in Social Networks (OSNs) like Twitter, they are gaining an increasing attention since a lot of connection are established between users without any previous knowledge. This highlights one of the key features of a lot of OSNs: the creation of relationships between users. Therefore, it is important to find new ways to provide interesting friendships suggestions. However, mining and analyzing data from large scale Social Networks can become critical in terms of computational resources. This is particularly true in the context of ubiquitous access, where resource-constrained mobile devices are used to access the social network services. To this end, designing architectures/solutions offering the possibility of operating in a Mobile Cloud scenario is of key importance. Accordingly, we present a new recommendation system scheme that tries to find the right trade-offs between the exploitation of the already existing links/relationships and the interest affinities between users. In particular, such scheme is based on an inherently parallel Hubs And Authorities algorithm together with similarity measures that, for scalability purposes, can be easily transposed in a cloud scenario. The first one let us leverage triadic closures while the second one takes into account homophily. The proposal is supported by an extensive performance analysis on publicly available Twitter data. In particular, we proved the effectiveness of the proposed recommendation system by using several performance metrics available in the literature which include precision, recall, F-measure and G-measure. The results show encouraging perspectives in terms of both effectiveness and scalability, that are driving our future research efforts.