Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Exploring social influence via posterior effect of word-of-mouth recommendations
Proceedings of the fifth ACM international conference on Web search and data mining
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Social recommendations have been found to increase the product adoption probability. However, very few studies have considered the impact of social opinions on the users' evaluation of the product. In social networks, many times users' opinions are not completely independent from their friends and users tend to change their rating such that they are more similar to the social opinions. Understanding this behavior is important for developing accurate recommendation systems, precise information flow models and to launch effective viral marketing campaigns. In order to understand this phenomenon, we propose a novel formulation for the users ratings where every expressed rating is considered as a function of the social opinion along with the user preference and item characteristics. The proposed method helps in improving the prediction accuracy of users' rating by more than 2% in presence of social influence. Additionally, the learned model parameters reveal the degree of conformity of users. Detailed analysis of user social conformity show that more than 76% of users tend to conform to their friends to some extent. On an average, user ratings become more positive in presence of the social influence.