Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Randomization tests for distinguishing social influence and homophily effects
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Social action tracking via noise tolerant time-varying factor graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding contexts of social influence in online social networks
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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As social networking is moving into the web, the study and exploitation of social correlation has emerged as a hot research topic. Most of these work consider binary social relations, called "friendships". However, online users tend to establish many friendships of varying degree of strength, e.g., relatives, friends, co-workers, and acquaintances. We argue that, due to their different degree of strength, different friend relationships will have greatly varying degrees of correlation and should be distinguished. Besides, social correlation is not the only factor driving user behavior. In this paper, we address the problem of learning the strength of the social correlation, user, item, and sparsity factors in online social networks. We propose a probabilistic model, Factor Weight Model, for learning these strengths which maximize the joint probability of the observed user behavior, i.e., actions on items. Different from existing methods, our model considers not only social correlation, but it also considers the other factors affecting user behavior. We have conducted experiments on four real life data sets from Epinions, Flixster, Flickr, and Digg. Our experiments prove the superiority of our model over a state-of-the-art method in terms of action prediction. We also analyze the contributions of the various factors for the prediction performance.