Research on Three-dimensional Personalized Recommendation Approach for C2C E-commerce Platform
Ai Danxiang1, Zuo Hui2, Yang Jun1
1. School of Management, Cuangdong University of Technology, Cuangzhou 510520, China; 2. School of Economics and Commerce, Guangdong University of Technology, Guangzhou 510520, China
Abstract：This paper defines a three-dimensional recommendation space and recommendation task in C2C e-commerce platforms, which are different from B2C ones, and proposes a three-dimensional personalized recommendation approach, which extends the traditional two-dimensional collaborative filtering method and content-based recommendation method. The proposed approach firstly calculates seller similarities using seller features, and fills the three-dimensional rating set based on sales relations and seller similarities to solve the data sparsity problem. Then it calculates buyer similarities using historical ratings to decide neighbors and predict unknown ratings. A true data experiment proves that the proposed approach is effective to solve the personalized recommendation problem in C2C platforms and has good performance when recommending seller and product combinations.
艾丹祥, 左晖, 杨君. 面向C2C电子商务平台的三维个性化推荐方法研究[J]. 现代图书情报技术, 2013, 29(1): 36-42.
Ai Danxiang, Zuo Hui, Yang Jun. Research on Three-dimensional Personalized Recommendation Approach for C2C E-commerce Platform. New Technology of Library and Information Service, 2013, 29(1): 36-42.
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