TY - GEN
T1 - Predicting usefulness of Yelp reviews with localized linear regression models
AU - Shen, Ruhui
AU - Shen, Jialiang
AU - Li, Yuhong
AU - Wang, Haohan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - Many websites such as Yelp provide platform for users to write reviews about places they have visited. But not all reviews are equally useful. However, it generally takes from several weeks to months to receive feedback about 'usefulness' of review from online community. So there is a need to automatically predict the 'usefulness' of review. In this paper, we are trying to solve the specific question 'How many 'useful' votes a Yelp review will receive?' by using bag-of-words, linguistic, geographical, statistical, popularity and other qualitative features extracted from user, business and review information provided by Yelp. We use state-of-The-Art machine learning algorithms for regression to predict required numeric value of 'usefulness' of review. We further gained performance improvement by introducing a batch mode localized weighted regression model. This localized regression approach resulted into RMSLE of 0.47769, which is better than traditional methods.
AB - Many websites such as Yelp provide platform for users to write reviews about places they have visited. But not all reviews are equally useful. However, it generally takes from several weeks to months to receive feedback about 'usefulness' of review from online community. So there is a need to automatically predict the 'usefulness' of review. In this paper, we are trying to solve the specific question 'How many 'useful' votes a Yelp review will receive?' by using bag-of-words, linguistic, geographical, statistical, popularity and other qualitative features extracted from user, business and review information provided by Yelp. We use state-of-The-Art machine learning algorithms for regression to predict required numeric value of 'usefulness' of review. We further gained performance improvement by introducing a batch mode localized weighted regression model. This localized regression approach resulted into RMSLE of 0.47769, which is better than traditional methods.
KW - linear regression
KW - localized model
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85016932894&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016932894&partnerID=8YFLogxK
U2 - 10.1109/ICSESS.2016.7883046
DO - 10.1109/ICSESS.2016.7883046
M3 - Conference contribution
AN - SCOPUS:85016932894
T3 - Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
SP - 189
EP - 192
BT - ICSESS 2016 - Proceedings of 2016 IEEE 7th International Conference on Software Engineering and Service Science
A2 - Babu, M. Surendra Prasad
A2 - Wenzheng, Li
PB - IEEE Computer Society
T2 - 7th IEEE International Conference on Software Engineering and Service Science, ICSESS 2016
Y2 - 26 August 2016 through 28 August 2016
ER -