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A product recommendation model based on online reviews: Improving PageRank algorithm considering attribute weights

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  • Wang, Xiaoli
  • Zhang, Chenxi
  • Xu, Zeshui

Abstract

Consumer reviews play a crucial role in evaluating products on online e-commerce platforms. Unlike numerical ratings, online reviews provide valuable information and sentiment. However, existing studies often overlook the unique interrelationships between products on e-commerce platforms, and fail to adequately capture the psychological behavior of consumers during online shopping. To address these gaps, this study presents a novel product recommendation model based on online reviews that evaluates products’ multi-attribute performances. The study first identifies the product attributes that are most important to consumers by analyzing review texts. Then, this study calculates the attribute performance scores of each product by considering consumer sentiment and the usefulness of online reviews. Next, it identifies competitors for the target product using a weighted Euclidean distance function and ranks all products employing an improved PageRank algorithm. Finally, to illustrate the validity of the proposed model, the study conducts a case study using a dataset of 41,352 online reviews obtained from Best Buy, and segments the data into three categories according to price. Comparative results with traditional MCDM models show that among the three categories, our results achieved a maximum improvement of 18.3% in the Spearman correlation coefficient.

Suggested Citation

  • Wang, Xiaoli & Zhang, Chenxi & Xu, Zeshui, 2024. "A product recommendation model based on online reviews: Improving PageRank algorithm considering attribute weights," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924003485
    DOI: 10.1016/j.jretconser.2024.104052
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    Cited by:

    1. Guo, Wenhao & Tian, Jin & Li, Minqiang, 2025. "Reputation vs. price: Sequential recommendations based on cue diagnosticity theory," Journal of Retailing and Consumer Services, Elsevier, vol. 83(C).
    2. Li, Yanlai & Yu, Huiru & Shen, Zifan, 2025. "Dynamic prediction of product competitive position: A multisource data-driven competitive analysis framework from a multi-competitor perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 85(C).
    3. Lu, Yangyang & Zhang, Jing, 2025. "Balancing identity diversity and product contexts: Understanding consumer trust in AI-enhanced chatbot services," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).

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