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

Lingling Zhang, Jing Li, Qiuliu Zhang, Fan Meng and Weili Teng
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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
Jing Li: School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China*School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
Qiuliu Zhang: School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China
Fan Meng: School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 102206, China
Weili Teng: Nottingham Business School, Nottingham Trent University, City Campus, Nottingham NG1 4BU, UK

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 01, 311-338

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: Link prediction; recommender system; domain knowledge; semantic similarity; time attenuation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219622018410031

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