An overview on the exploitation of time in collaborative filtering

Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user‐generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the system—and old ones leaving—user and item activity rate fluctuations and other similar time‐related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future. WIREs Data Mining Knowl Discov 2015, 5:195–215. doi: 10.1002/widm.1160 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Data Mining Software Tools