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Calibration of Voting-Based Helpfulness Measurement for Online Reviews : An Iterative Bayesian Probability Approach

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

Ketersediaan

Informasi Detail

Judul Seri

INFORMS JOURNAL ON COMPUTING; Vol.33 No.1 Winter 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 246 - 261

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ONLINE REVIEWS
PREDICTIVE ANALYTICS
HELPFULNESS PREDICTION
SOCIAL VOTING
BAYESIAN PROBABILITY
ITERATIVE ESTIMATION

Info Detail Spesifik

https://doi.org/10.1287/ijoc.2019.0951

Pernyataan Tanggungjawab

Xunhua Guo, Guoqing Chen, Cong Wang, Qiang Wei, Zunqiang Zhang

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