Temporal collaborative filtering with adaptive neighbourhoods

Neal Lathia, Stephen Hailes, Licia Capra
2009 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09  
Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, after learning from a training set. However, deployed recommender systems do not operate on, and should not be optimised to predict, a static set of user ratings because the underlying dataset is continuously growing and changing. The aim of a recommender system is therefore to iteratively predict users' preferences over a
more » ... preferences over a dynamic dataset, and system administrators are confronted with the problem of having to continuously tune the parameters calibrating their CF algorithm for best performance. In this work, we first formalise CF as a time-dependent, iterative prediction problem. We then perform a temporal analysis of the Netflix dataset, and evaluate the temporal performance of a baseline model and the k-Nearest Neighbour algorithm. We show that, due to the dynamic nature of the data, certain prediction methods that improve prediction accuracy on the Netflix probe set do not show similar improvements over a set of iterative train-test experiments with growing data. We then address the problem of parameter selection and update, and propose a method to automatically assign and update per-user neighbourhood sizes that (on the temporal scale) outperforms setting global parameters.
doi:10.1145/1571941.1572133 dblp:conf/sigir/LathiaHC09 fatcat:iofhejccqzdudd6resztlu5tye