Title: A unified framework for link and rating prediction in multi-modal social networks

Authors: Panagiotis Symeonidis; Eleftherios Tiakas; Yannis Manolopoulos

Addresses: Department of Informatics, Aristotle University, Thessaloniki, 54124, Greece ' Department of Informatics, Aristotle University, Thessaloniki, 54124, Greece ' Department of Informatics, Aristotle University, Thessaloniki, 54124, Greece

Abstract: Multi-modal social networks (MSNs) allow users to form explicit (by adding new friends in their network) or implicit (by similarly co-rating items) social networks. Previous research work was limited either to the prediction of new relationships among users (i.e., link prediction problem) or to the prediction of item ratings (i.e., rating prediction problem and item recommendations). In this paper, we develop a framework to incorporate both research directions into a unified model. Our social-union algorithm combines similarity matrices derived from heterogeneous (unipartite and bipartite) explicit or implicit MSNs. We perform an extensive experimental comparison of the proposed method against existing link and rating prediction algorithms, using synthetic and two real data sets (Epinions and Flixter). Our experimental results show that our social-union framework is more effective in both rating and link prediction.

Keywords: data mining; social network mining; link prediction; item recommendation; rating prediction; multi-modal social networks.

DOI: 10.1504/IJSNM.2013.059066

International Journal of Social Network Mining, 2013 Vol.1 No.3/4, pp.225 - 253

Available online: 03 Feb 2014 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article