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Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation

Lingling Zhang

School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing, 100190, China

E-mail Address: zhangll@ucas.ac.cn

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Jing Li

School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

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Qiuliu Zhang

School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

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Fan Meng

School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 102206, China

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, and

Weili Teng

Nottingham Business School, Nottingham Trent University, City Campus, Nottingham NG1 4BU, UK

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https://doi.org/10.1142/S0219622018410031Cited by:17 (Source: Crossref)

Abstract

In this paper, we propose domain knowledge-based link prediction algorithm in customer-product bipartite network to improve effectiveness of product recommendation in retail. The domain knowledge is classified into product domain knowledge and time context knowledge, which play an important part in link prediction. We take both of them into consideration in recommendation and form a unified domain knowledge-based link prediction framework. We capture product semantic similarity by ontology-based analysis and time attenuation factor from time context knowledge, then incorporate them into network topological similarity to form a new linkage measure. To evaluate the algorithm, we use a real retail transaction dataset from Food Mart. Experimental results demonstrate that the usage of domain knowledge in link prediction achieved significantly better performance.

Keywords:

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