A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
What Have the Neighbours Ever Done for Us? A Collaborative Filtering Perspective
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A Visual Interface for Social Information Filtering
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Social networks and interest similarity: the case of CiteULike
Proceedings of the 21st ACM conference on Hypertext and hypermedia
A matrix factorization technique with trust propagation for recommendation in social networks
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
Recommender Systems Handbook
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
An analysis of topical proximity in the twitter social graph
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
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The explosive growth of online social networks in recent times has presented a powerful source of information to be utilised in personalised recommendations. Unsurprisingly there has already been a large body of work completed in the recommender system field to incorporate this social information into the recommendation process. In this paper we examine the practice of leveraging a user's social graph in order to generate recommendations. Using various neighbourhood selection strategies, we examine the user satisfaction and the level of perceived trust in the recommendations received.