Research on E-Commerce Platform-Based Personalized Recommendation Algorithm
Joint Authors
Zhang, Zhijun
Xu, Gongwen
Zhang, Pengfei
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-20
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level.
In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time.
The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.
American Psychological Association (APA)
Zhang, Zhijun& Xu, Gongwen& Zhang, Pengfei. 2016. Research on E-Commerce Platform-Based Personalized Recommendation Algorithm. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1094904
Modern Language Association (MLA)
Zhang, Zhijun…[et al.]. Research on E-Commerce Platform-Based Personalized Recommendation Algorithm. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1094904
American Medical Association (AMA)
Zhang, Zhijun& Xu, Gongwen& Zhang, Pengfei. Research on E-Commerce Platform-Based Personalized Recommendation Algorithm. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1094904
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094904