Volume 5 Number 7 (Jul. 2010)
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JSW 2010 Vol.5(7): 729-736 ISSN: 1796-217X
doi: 10.4304/jsw.5.7.729-736

Incorporating Personalized Contextual Information in Item-based Collaborative Filtering Recommendation

Min Gao, Zhongfu Wu
College of Computer Science Chongqing University, Chongqing, China, 400044

Abstract—After reviewing the prior work and problem of collaborative filtering recommendation approaches, an approach incorporating personalized contextual information in item-based collaborative filtering is proposed to solve the problem. The approach provides recommendations based on user personalized contextual information besides the typical information on users and items used in most of the current recommendation systems. In this paper, several approaches are proposed to calculate context-based item differences, learn personalized contextual information for every user and predict ratings based on well-known item-based collaborative filtering Slope One. Finally, we experimentally evaluate our approach and compare it to Slope One. The experimental results show that our approach provide more precision recommendations than Slope One.

Index Terms—recommendation; context; personalization; collaboration filtering.

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Cite: Min Gao, Zhongfu Wu, "Incorporating Personalized Contextual Information in Item-based Collaborative Filtering Recommendation," Journal of Software vol. 5, no. 7, pp. 729-736, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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