An overview on the exploitation of time in collaborative filtering

• Type: Another Publication in an International Scientific Journal
• Date: 2015

Authors

Joao Vinagre (Author) Other; Alipio Mario Jorge (Author) FCUP; Joao Gama (Author) FEP

Journal

Title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Vol. 5

Pages: 195-215

ISSN: 1942-4787

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 26 Citations

Scopus - 33 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00G-FZE

DOI: 10.1002/widm.1160

Abstract (EN): 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 systemand old ones leavinguser 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.(C) 2015 John Wiley & Sons, Ltd.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21