GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Proceedings of the 2007 ACM conference on Recommender systems
Web Semantics: Science, Services and Agents on the World Wide Web
Tag-based user modeling for social multi-device adaptive guides
User Modeling and User-Adapted Interaction
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Towards effective recommendation of social data across social networking sites
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Double-sided recommendations: a novel framework for recommender systems
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
SocConnect: A personalized social network aggregator and recommender
Information Processing and Management: an International Journal
Knowledge-Based Systems
Expediting expertise: supporting informal social learning in the enterprise
Proceedings of the 19th international conference on Intelligent User Interfaces
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User modeling systems have been influenced by the overspread of Web 2.0 and social networks. New systems aimed at helping people finding information of interest and including "social functions" like social networks, tagging, commenting, inserting content, arose. Such systems are the so-called "social recommender systems". The idea at the base of social recommender systems is that the recommendation of content should follow user's preferences while social network just represents a group of users joined by some kind of voluntary relation and does not reflect any preference. We claim that social network is a very important source of information to profile users. Moving from theories in social psychology which describe influence dynamics among individuals, we state that joining in a network with other people exposes individuals to social dynamics which can influence their attitudes, behaviours and preferences. We present in this paper SoNARS , a new algorithm for recommending content in social recommender systems. SoNARS targets users as members of social networks, suggesting items that reflect the trend of the network itself, based on its structure and on the influence relationships among users.