IEICE TRANSACTIONS on Information
Detecting Anomalous Reviewers and Estimating Summaries from Early Reviews Considering Heterogeneity
Summary :
Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers' degree of anomaly and estimating long-term evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.1003-1011
- Publication Date
- 2018/04/01
- Publicized
- 2018/01/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017DAP0006
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Yasuhito ASANO
Kyoto University
Junpei KAWAMOTO
Kyushu University
Keyword
Latest Issue
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Yasuhito ASANO, Junpei KAWAMOTO, "Detecting Anomalous Reviewers and Estimating Summaries from Early Reviews Considering Heterogeneity" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 4, pp. 1003-1011, April 2018, doi: 10.1587/transinf.2017DAP0006.
Abstract: Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers' degree of anomaly and estimating long-term evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017DAP0006/_p
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@ARTICLE{e101-d_4_1003,
author={Yasuhito ASANO, Junpei KAWAMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Anomalous Reviewers and Estimating Summaries from Early Reviews Considering Heterogeneity},
year={2018},
volume={E101-D},
number={4},
pages={1003-1011},
abstract={Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers' degree of anomaly and estimating long-term evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies.},
keywords={},
doi={10.1587/transinf.2017DAP0006},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Detecting Anomalous Reviewers and Estimating Summaries from Early Reviews Considering Heterogeneity
T2 - IEICE TRANSACTIONS on Information
SP - 1003
EP - 1011
AU - Yasuhito ASANO
AU - Junpei KAWAMOTO
PY - 2018
DO - 10.1587/transinf.2017DAP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers' degree of anomaly and estimating long-term evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies.
ER -
English
