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An empirical study on effectiveness of temporal information as implicit ratings

Cited 19 time in Web of Science Cited 23 time in Scopus
Authors
Lee, Tong Queue; Park, Young; Park, Yong-Tae
Issue Date
2009-03-01
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
EXPERT SYSTEMS WITH APPLICATIONS; Vol.36 2; 1315-1321
Keywords
E-commerceExperimentsRecommendation accuracyImplicit ratingsRecommender systemTemporal informationCollaborative filtering
Abstract
Collaborative filtering is a widely used and proven method of building recommender systems, which provide personalize(] recommendations oil products or services based oil explicit ratings from users. Recommendation accuracy becomes an especially important factor in sonic e-commerce environments (such as a mobile environment, due to limited connection time and device size). AS user preferences change over time. temporal information call improve recommendation accuracy. This paper presents a variety of temporal information including item launch time, user buying time. the time difference between the two, as well as several combinations of these three. We conducted an empirical study on how temporal information affects the accuracy of a collaborative filtering system for recommending character images (wallpapers) in I mobile c-commerce environment. Empirical results show the degree of effectiveness of a variety of temporal information. The empirical results give insight oil how to incorporate temporal information to maximize the effectiveness of collaborative filtering in various c-commerce environments. (c) 2007 Elsevier Ltd. All rights reserved.
ISSN
0957-4174
Language
English
URI
https://hdl.handle.net/10371/75328
DOI
https://doi.org/10.1016/j.eswa.2007.11.047
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
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