A Recommendation Method in E-Commerce Based on Product Taxonomy Graph

Qian Liu (), Hongzhi Wang (), Hong Gao (), Qi Lv () and Jianyu Fu ()
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Qian Liu: Harbin Institute of Technology
Hongzhi Wang: Harbin Institute of Technology
Hong Gao: Harbin Institute of Technology
Qi Lv: Harbin Institute of Technology
Jianyu Fu: Harbin Institute of Technology

A chapter in 2012 International Conference on Information Technology and Management Science(ICITMS 2012) Proceedings, 2013, pp 411-422 from Springer

Abstract: Abstract The data of e-commerce is growing at a rapid speed. As a result, customers are no longer able to achieve what they want to buy in a relatively short time. Collaborative Filtering (CF) is the most acceptable method about recommendation. However it has two limitations. One is sparsity, the other is scalability. In this paper we give a methodology to solve the problems based on product taxonomy graph. Data mining on product taxonomy graph helps make the transaction data in more aggregated way which is expected to solve the sparsity and scalability problem in CF.

Keywords: CF; Product taxonomy graph; Recommendation; Top-k (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-34910-2_47

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DOI: 10.1007/978-3-642-34910-2_47

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