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Soft Computing

, Volume 22, Issue 16, pp 5247–5261 | Cite as

Supporting consumer’s purchase decision: a method for ranking products based on online multi-attribute product ratings

  • Zhi-Ping Fan
  • Yang Xi
  • Yang Liu
Focus

Abstract

Online product ratings, as a type of electronic word-of-mouth, play an important role for helping consumers select desirable products, but it is difficult for consumers to read a large number of online ratings on e-commerce Web site. To support consumer’s purchase decision, how to rank the candidate products based on online product ratings and consumer’s preferences is a noteworthy research topic, while the existing studies concerning this issue are still relatively scarce. This paper proposes a method for ranking products based on online multi-attribute product ratings. In the method, a discrete percentage distribution of the evaluation of each candidate product with respect to each attribute based on online ratings is first constructed, and the \(3\sigma \) criterion is used to eliminate the anomalous ratings. Then, by defining of the stochastic dominance rules and the stochastic dominance degrees on comparing two discrete percentage distributions, the stochastic dominance relation between each pair of products is determined, and the corresponding stochastic dominance degree is calculated. Further, according to the obtained stochastic dominance degrees, the ranking of candidate products can be determined using the PROMETHEE-II method. A case study on selecting the automobile is given to illustrate the use of the proposed method.

Keywords

Product ranking Online ratings Discrete percentage distribution Stochastic dominance degree PROMETHEE-II method 

Notes

Acknowledgements

This study was funded by the National Science Foundation of China (Project Nos. 71571039, 71771043 and 71371002), the Fundamental Research Funds for the Central Universities, NEU, China (Project No. N140607001), and the 111 Project (B16009).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Information Management and Decision Sciences, School of Business AdministrationNortheastern UniversityShenyangChina
  2. 2.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

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