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21-05-2019

ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites

Authors: Yayuan Tang, Kehua Guo, Ruifang Zhang, Tao Xu, Jianhua Ma, Tao Chi

Published in: World Wide Web | Issue 2/2020

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Abstract

To solve the problem that users’ retrieval intentions are seldom considered by personalized websites, we propose an improved incremental collaborative filtering (CF)-based recommendation implementation method (ICFR) in this paper. The ICFR model uses one of the most popular recommendation algorithms – the collaborative filtering recommendation algorithm – for personalized websites. This paper first uses a CF algorithm to obtain the relationship between user preferences and recommended content. Second, the browsing behaviour information of users is extracted by analysing Web logs and is then converted into ratings. Finally, an incremental algorithm is designed to update historical user preference data. Based on this established model, we propose some cases for this architecture, which illustrate that the ICFR model is suitable for personalized website recommendations.

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Title: ICFR: An effective incremental collaborative filtering based recommendation architecture for personalized websites
Authors: Yayuan Tang
Kehua Guo
Ruifang Zhang
Tao Xu
Jianhua Ma
Tao Chi
Publication date: 21-05-2019
Publisher: Springer US
Published in: World Wide Web / Issue 2/2020
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-019-00693-x

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