An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Relation Grid: A Social Relationship Network Model
SKG '05 Proceedings of the First International Conference on Semantics, Knowledge and Grid
Feature weighting in content based recommendation system using social network analysis
Proceedings of the 17th international conference on World Wide Web
SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Recommender systems and the social web
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
An empirical comparison of social, collaborative filtering, and hybrid recommenders
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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Traditional recommendation systems provide appropriate information to a target user after analyzing user preferences based on user profiles and rating histories. However, most of people also consider the friend's opinions when they purchase some products or watch the movies. As social network services have been recently popularized, many users obtain and exchange their opinions on social networks. This information is reliable because they have close relationships and trust each other. Most of the users are satisfied with the information. In this paper, we propose a recommendation system based on advanced user modeling using social relationship of users. For the user modeling, both direct and indirect relations are considered and the relation weight between users is calculated by using six degrees of Kevin Bacon. From the experimental results, our proposed social filtering method can achieve better performance than a traditional user-based collaborative filtering method.