Referral Web: combining social networks and collaborative filtering
Communications of the ACM
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Recommendations in taste related domains: collaborative filtering vs. social filtering
Proceedings of the 2007 international ACM conference on Supporting group work
Recommending topics for self-descriptions in online user profiles
Proceedings of the 2008 ACM conference on Recommender systems
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Large-scale social recommender systems: challenges and opportunities
Proceedings of the 22nd international conference on World Wide Web companion
Proceedings of the 7th ACM conference on Recommender systems
Hi-index | 0.00 |
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.