Recommendation Using Textual Opinions
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2684-2689.
Abstract
Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users' review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders.
@inproceedings{ijcai2013p395,
title = {Recommendation Using Textual Opinions},
author = {Musat, Claudiu Cristian and Liang, Yizhong and Faltings, Boi},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2684--2689},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/395},
}
TY - CPAPER TI - Recommendation Using Textual Opinions AU - Musat, Claudiu Cristian AU - Liang, Yizhong AU - Faltings, Boi T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2684 EP - 2689 UR - https://ijcai.org/proceedings/2013/395 ER -Copied
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