%0 Book Section %1 MatuszykSpiliopoulouUmap14 %A Matuszyk, Pawel %A Spiliopoulou, Myra %B User Modeling, Adaptation, and Personalization %D 2014 %E Dimitrova, Vania %E Kuflik, Tsvi %E Chin, David %E Ricci, Francesco %E Dolog, Peter %E Houben, Geert-Jan %I Springer International Publishing %K from:matuszyk %P 146–157 %R 10.1007/978-3-319-08786-3_13 %T Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users %U https://kmd.cs.ovgu.de/pub/matuszyk/MatuszykSpiliopoulou14.pdf %V 8538 %X Neighbourhood-based collaborative filtering recommenders exploit the common ratings among users to identify a user’s most similar neighbours. It is known that decisions made on a naive computation of user similarity are unreliable, because the number of co-ratings varies strongly among users. In this paper, we formalize the notion of reliable similarity between two users and propose a method that constructs a user’s neighbourhood by selecting only those users that are reliably similar to her. Our method combines a statistical test and the notion of a baseline user. We report our results on typical benchmark datasets. %@ 978-3-319-08785-6

@incollection{MatuszykSpiliopoulouUmap14, abstract = {Neighbourhood-based collaborative filtering recommenders exploit the common ratings among users to identify a user’s most similar neighbours. It is known that decisions made on a naive computation of user similarity are unreliable, because the number of co-ratings varies strongly among users. In this paper, we formalize the notion of reliable similarity between two users and propose a method that constructs a user’s neighbourhood by selecting only those users that are reliably similar to her. Our method combines a statistical test and the notion of a baseline user. We report our results on typical benchmark datasets.}, added-at = {2015-10-15T21:30:56.000+0200}, author = {Matuszyk, Pawel and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/2d18c25896610735069aa41edad89c1c0/kmd-ovgu}, booktitle = {User Modeling, Adaptation, and Personalization}, doi = {10.1007/978-3-319-08786-3_13}, editor = {Dimitrova, Vania and Kuflik, Tsvi and Chin, David and Ricci, Francesco and Dolog, Peter and Houben, Geert-Jan}, interhash = {469167b771e8cae285e1ed9f663c26b0}, intrahash = {d18c25896610735069aa41edad89c1c0}, isbn = {978-3-319-08785-6}, keywords = {from:matuszyk}, language = {English}, pages = {146–157}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, timestamp = {2018-03-09T12:44:05.000+0100}, title = {Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users}, url = {https://kmd.cs.ovgu.de/pub/matuszyk/MatuszykSpiliopoulou14.pdf}, volume = 8538, year = 2014 }