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Online social sharing sites are becoming very popular nowadays among Web users, who use these sites to share their favourite items and to discover interesting and useful items from other users. While an explicit social network is not necessarily present in these sites, it is still possible that users influence one another in the process of item adoption through various implicit mechanisms. In this paper, we study how we can capture the implicit influences among the users in a social sharing site. We propose a probabilistic model for user adoption behaviour, where we assume that when user adopts an item, he would pick a user and choose from the set of items that this user has adopted. By using the model, we estimate the probability that one user influences another user in the course of item adoption, based on the temporal adoption pattern of the users. We carry out empirical studies of the model on Delicious, a popular social bookmarking site. Experiments show that our model can be used to predict item adoption more accurately than using collaborative filtering techniques. We find that the strength of implicit influence various across different topics. We also show that our method i

Author

Ching-man Au Yeung; Tomoharu Iwata

Publisher

ACM

Published

2010

Language

EN