Temporal collaborative filtering with adaptive neighbourhoods

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 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 metho...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Neal Lathia, Stephen Hailes, Licia Capra
• Keywords: Information Retrieval, Iterative Prediction Problem, Recommender System, SIGIR 2009, User Ratings