The process of decision making in humans involves a combination of the genuine information held by the individual, and the external influence from their social network connections. This helps individuals to make decisions or adopt behaviors, opinions or products. In this work, we seek to investigate under which conditions and with what cost we can form neighborhoods of influence within a social network, in order to assist individuals with little or no prior genuine information through a two-phase recommendation process. Most of the existing approaches regard the problem of identifying influentials as a long-term, network diffusion process, where information cascading occurs in several rounds and has fixed number of influentials. In our approach we consider only one round of influence, which finds applications in settings where timely influence is vital. We tackle the problem by proposing a two-phase framework that aims at identifying influentials in the first phase and form influential neighborhoods to generate recommendations to users with no prior knowledge in the second phase. The difference of the proposed framework with most social recommender systems is that we need to generate recommendations including more than one item and in the absence of explicit ratings, solely relying on the social network's graph.
Magdalini Eirinaki, Nuno Moniz, and Katerina Potika. "Threshold-Bounded Influence Dominating Sets for Recommendations in Social Networks" Proceedings of the 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) (2016). doi:10.1109/BDCloud-SocialCom-SustainCom.2016.67