The adoption of artificial intelligence (AI) and machine learning (ML) in risk-sensitive
environments is still in its infancy because it lacks a systematic framework for reasoning …

The current review helpfulness prediction (RHP) methods simply rely on the textual features
and meta features to predict review helpfulness, overlooking the informational value of …

The problem of information overload in online review platforms has seriously hampered
many customers' ability to evaluate the quality of products or businesses when making …

Online reviews are integral to consumer decision-making while purchasing products on an e-
commerce platform. Extant literature has conclusively established the effects of various …

Abstract Information overload often makes it difficult for consumers to identify valuable online
reviews through the traditional “helpful votes” button in the big data era, so it is essential to …

This comprehensive survey investigates methodologies and factors utilized for predicting
review helpfulness on e-commerce websites. Analyzing 132 research publications from the …

As digital healthcare evolves, the security of electronic health records (EHR) becomes
increasingly crucial. This study presents the GPT-Onto-CAABAC framework, integrating …

Review helpfulness prediction aims to provide helpful reviews for customers to make
purchase decisions. Although many studies have proposed prediction mechanisms, few …

Purpose This paper seeks to propose and empirically validate a conceptual model on the
antecedents of review helpfulness comprising three constructs, namely, valence …

A job review is an essential source of information for job seekers. Since job reviews
influence a job application that can be a life-changing decision, detecting helpful reviews is …