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An overview on the exploitation of time in collaborative filtering
Author(s) -
Vinagre João,
Jorge Alípio Mário,
Gama João
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1160
Subject(s) - collaborative filtering , computer science , key (lock) , dimension (graph theory) , preference , data mining , contrast (vision) , recommender system , software , data science , machine learning , artificial intelligence , statistics , mathematics , computer security , pure mathematics , programming language
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