Credible Review Detection with Limited Information Using Consistency Features
Abstract
Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers’ purchasing decisions. However, the proliferation of non-credible reviews — either fake (promoting/ demoting an item), incompetent (involving irrelevant aspects), or biased — entails the problem of identifying credible reviews. Prior works involve classifiers harnessing rich information about items/users — which might not be readily available in several domains — that provide only limited interpretability as to why a review is deemed non-credible.
Cite
Text
Mukherjee et al. "Credible Review Detection with Limited Information Using Consistency Features." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46227-1_13Markdown
[Mukherjee et al. "Credible Review Detection with Limited Information Using Consistency Features." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/mukherjee2016ecmlpkdd-credible/) doi:10.1007/978-3-319-46227-1_13BibTeX
@inproceedings{mukherjee2016ecmlpkdd-credible,
title = {{Credible Review Detection with Limited Information Using Consistency Features}},
author = {Mukherjee, Subhabrata and Dutta, Sourav and Weikum, Gerhard},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {195-213},
doi = {10.1007/978-3-319-46227-1_13},
url = {https://mlanthology.org/ecmlpkdd/2016/mukherjee2016ecmlpkdd-credible/}
}