Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
Investigating interactions of trust and interest similarity
Decision Support Systems
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
Using a trust network to improve top-N recommendation
Proceedings of the third ACM conference on Recommender systems
How to measure the information similarity in unilateral relations: the case study of Delicious
Proceedings of the International Workshop on Modeling Social Media
Using self-defined group activities for improvingrecommendations in collaborative tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Proceedings of the Workshop on Context-Aware Movie Recommendation
Improving recommendations using WatchingNetworks in a social tagging system
Proceedings of the 2011 iConference
Leveraging the linkedin social network data for extracting content-based user profiles
Proceedings of the fifth ACM conference on Recommender systems
Power to the people: exploring neighbourhood formations in social recommender system
Proceedings of the fifth ACM conference on Recommender systems
We love rock 'n' roll: analyzing and predicting friendship links in Last.fm
Proceedings of the 3rd Annual ACM Web Science Conference
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
Combining social information for academic networking
Proceedings of the 2013 conference on Computer supported cooperative work
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In collaborative filtering recommender systems, there is little room for users to get involved in the choice of their peer group. It leaves users defenseless against various spamming or ''shilling'' attacks. Other social Web-based systems, however, allow users to self-select peers and build a social network. We argue that users' self-defined social networks could be valuable to increase the quality of recommendation in CF systems. To prove the feasibility of this idea we examined how similar are interests of users connected by self-defined relationships in a collaborative tagging systems Citeulike. Interest similarity was measured by similarity of items and meta-data they share and tags they use. Our study shows that users connected by social networks exhibit significantly higher similarity on all explored levels (items, meta-data, and tags) than non-connected users. This similarity is the highest for directly connected users and decreases with the increase of distance between users. Among other interesting properties of information sharing is the finding that between-user similarity in social connections on the level of metadata and tags is much larger than similarity on the level of items. Overall, our findings support the feasibility of social network based recommender systems and offer some good hints to the prospective authors of these systems.