GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 10th international conference on Intelligent user interfaces
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Trust and nuanced profile similarity in online social networks
ACM Transactions on the Web (TWEB)
Using trust in collaborative filtering recommendation
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Modern Information Retrieval
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
ACM Transactions on the Web (TWEB)
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User-based collaborative filtering approaches suggest interesting items to a user relying on similar-minded people referred to as neighbours. While standard approaches select neighbours based on user similarity, others rely on aspects related to user trustworthiness and reliability. We investigate the extent to which user similarities are essential to obtain high quality item recommendation, and propose to select neighbours according to the overlap of their preferences with those of the target user. We empirically show that a neighbour selection strategy based on preference overlap achieves better performance than similarity- and trust-based selection strategies, in terms of both recommendation accuracy and precision.