Communications of the ACM
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Social Recommendation with Interpersonal Influence
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search and data mining
Social contextual recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
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We propose a recommendation method that considers the user's individual preference and influence from other users in social media. This method predicts the user's individual preference and influence from other users by applying the probability of divergence from random-selection based on a statistical hypothesis test as a form of modified content-based filtering. We evaluated the proposed method by focusing on the rate at which items that have recommended tags are contained among all items. The proposed method is shown to have higher accuracy than traditional content-based filtering. It is especially effective when some percentage of the items have recommendation tags.