TY - GEN
T1 - Context-aware review helpfulness rating prediction
AU - Tang, Jiliang
AU - Gao, Huiji
AU - Hu, Xia
AU - Liu, Huan
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.
AB - Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.
KW - Content context
KW - Review rating prediction
KW - Review recommendation
KW - Social context
UR - http://www.scopus.com/inward/record.url?scp=84887569719&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887569719&partnerID=8YFLogxK
U2 - 10.1145/2507157.2507183
DO - 10.1145/2507157.2507183
M3 - Conference contribution
AN - SCOPUS:84887569719
SN - 9781450324090
T3 - RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
SP - 1
EP - 8
BT - RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems
T2 - 7th ACM Conference on Recommender Systems, RecSys 2013
Y2 - 12 October 2013 through 16 October 2013
ER -