Mohammad Shafkat Amin, Baoshi Yan, Sripad Sriram, Anmol Bhasin, Christian Posse

RecSys '12 Proceedings of the sixth ACM conference on Recommender systems


 

Abstract

Much work has been done to study the interplay between recommender systems and social networks. This creates a very powerful coupling in presenting highly relevant recommendations to the users. However, to our knowledge, little attention has been paid to leverage a user's social network to deliver these recommendations. We present a novel approach to aid delivery of recommendations using the recipient's friends or connections. Our contributions with this study are 1) A novel recommendation delivery paradigm called Social Referral, which utilizes a user's social network for the delivery of relevant content. 2) An implementation of the paradigm is described in a real industrial production setting of a large online professional network. 3) A study of the interaction between the trifecta of the recommender system, the trusted connections and the end consumer of the recommendation by comparing and contrasting the proposed approach's performance with the direct recommender system.

Our experiments indicate that Social Referral is a promising mechanism for recommendation delivery. The experiments show that a significant portion of users are receptive to passing along relevant recommendations to their social networks, and that recommendations delivered through users' social networks are much more likely to be accepted than those directly delivered to users.