Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website

Nan Jing (), Tao Jiang (), Juan Du () and Vijayan Sugumaran ()
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Nan Jing: Shanghai University
Tao Jiang: Shanghai University
Juan Du: Shanghai University
Vijayan Sugumaran: Oakland University

Electronic Commerce Research, 2018, vol. 18, issue 1, No 9, 159-179

Abstract: Abstract In the era of electronic commerce, online customer reviews (OCRs) have become a prevalent and valuable information source for both customers and merchants to make business decisions. This paper proposes an enhanced collaborative filtering approach based on sentiment assessment to discover the potential preferences of customers, and to predict customers’ future requirements for business services or products (collectively referred to as “entities”). Specifically, this approach involves three major steps: aspect-level sentiment assessment, customer preference mining and personalized recommendation. First, the aspect-level sentiment assessment transforms OCRs to a structured aspect-level review vector. Second, customer preference mining uses the vector to extract aspect-level feature words from sentiments and assigns polarity score to each sentiment. Finally, the feature words and sentiment polarity score are used to calculate customer preference and customers’ similarities. Personalized recommendation for services and products are generated according to customer similarity. Experiments are conducted based on the data from one of the most popular electronic commerce websites in China ( www.JD.com ). The results demonstrate that the proposed approach outperforms traditional collaborative filtering approaches in effectively recommending entities to target customers especially in the long term.

Keywords: Online customer reviews (OCRs); Collaborative filtering; Sentiment assessment; Personalized recommendation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10660-017-9275-6

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