TY - GEN
T1 - Online evaluation re-scoring based on review behavior analysis
AU - Zhang, Rong
AU - He, Xiaofeng
AU - Zhou, Aoying
AU - Sha, Chaofeng
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/10
Y1 - 2014/10/10
N2 - Customer reviews written at online shopping sites greatly influence the decision of potential buyers. Since existence of noise in reviews is inevitable, helping users alleviate the influence of these noisy reviews has become a fundamental issue for improving service quality in e-commerce transactions, especially for C2C (customer-to-customer) sites. In this paper, we present an approach to reduce the influence of noisy review and improve product ranking quality by using customer credibility. Customer credibility is used to measure to what degree the reviews can be trusted. A feedback strategy is designed to calculate the customer credibility, which relies on the consistency evaluation between individual reviews and overall reviews. Additionally, we provide a method to eliminate the inconsistency problem between the review comments and customer given scores, captured by the learned model on the training data that is constructed automatically. The final product scores are calculated by considering both the customer credibility and the predicted scores. The experimental results on real-world data sets show that our proposed approach provides better products ranking than baseline systems.
AB - Customer reviews written at online shopping sites greatly influence the decision of potential buyers. Since existence of noise in reviews is inevitable, helping users alleviate the influence of these noisy reviews has become a fundamental issue for improving service quality in e-commerce transactions, especially for C2C (customer-to-customer) sites. In this paper, we present an approach to reduce the influence of noisy review and improve product ranking quality by using customer credibility. Customer credibility is used to measure to what degree the reviews can be trusted. A feedback strategy is designed to calculate the customer credibility, which relies on the consistency evaluation between individual reviews and overall reviews. Additionally, we provide a method to eliminate the inconsistency problem between the review comments and customer given scores, captured by the learned model on the training data that is constructed automatically. The final product scores are calculated by considering both the customer credibility and the predicted scores. The experimental results on real-world data sets show that our proposed approach provides better products ranking than baseline systems.
UR - https://www.scopus.com/pages/publications/84911201070
U2 - 10.1109/ASONAM.2014.6921558
DO - 10.1109/ASONAM.2014.6921558
M3 - 会议稿件
AN - SCOPUS:84911201070
T3 - ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
SP - 43
EP - 50
BT - ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
A2 - Wu, Xindong
A2 - Wu, Xindong
A2 - Ester, Martin
A2 - Xu, Guandong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
Y2 - 17 August 2014 through 20 August 2014
ER -