Knowledge-sharing and influence in online social networks via viral marketing
Communications of the ACM - Mobile computing opportunities and challenges
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM conference on Electronic commerce
Exploring social influence for recommendation: a generative model approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we analyze a recommendation network with over 4,000 users and half a million books. There are two types of edges in this network, "read" relations between users and books, and following relations between users. We first investigate in general, if one's followees' recommendations have impacts on one's decision. We then analyze the correlation between one's influence and her centrality in the network. Finally, we study how effective a recommendation is as one sends or receives more and more recommendations. Results show that although in general, one's followee do have an impact over her decision, such influence is not correlated with the followee's centrality. As one receives more and more recommendations for a product, it is more likely that she will accept it. However, there is a saturate point over which more recommendations will have no further impact. As one sends out more and more recommendations, the probabilities that these recommendations get accepted become larger and larger.