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Applied Computational Intelligence and Soft Computing
Volume 2016 (2016), Article ID 5160460, 7 pages
http://dx.doi.org/10.1155/2016/5160460
Research Article

Research on E-Commerce Platform-Based Personalized Recommendation Algorithm

School of Computer Science and Technology, Shandong Jianzhu University, Jinan, Shandong 250101, China

Received 22 February 2016; Accepted 26 June 2016

Academic Editor: Francesco Carlo Morabito

Copyright © 2016 Zhijun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.