TY - GEN
T1 - Exploiting emotion on reviews for recommender systems
AU - Meng, Xuying
AU - Wang, Suhang
AU - Liu, Huan
AU - Zhang, Yujun
N1 - Funding Information: This work is supported by, or in part by, National Science Foundation of China (61672500, 61572474 and 61402446), and Program of International S&T Cooperation (2016YFE0121500). Suhang Wang and Huan Liu are supported by the National Science Foundation (NSF) under the grant #1614576 and Office of Naval Research (ONR) under the grant N00014-16-1-2257.
PY - 2018
Y1 - 2018
N2 - Review history is widely used by recommender systems to infer users' preferences and help find the potential interests from the huge volumes of data, whereas it also brings in great concerns on the sparsity and cold-start problems due to its inadequacy. Psychology and sociology research has shown that emotion information is a strong indicator for users' preferences. Meanwhile, with the fast development of online services, users are willing to express their emotion on others' reviews, which makes the emotion information pervasively available. Besides, recent research shows that the number of emotion on reviews is always much larger than the number of reviews. Therefore incorporating emotion on reviews may help to alleviate the data sparsity and cold-start problems for recommender systems. In this paper, we provide a principled and mathematical way to exploit both positive and negative emotion on reviews, and propose a novel framework MIRROR, exploiting eMotIon on Reviews for RecOmmendeR systems from both global and local perspectives. Empirical results on real-world datasets demonstrate the effectiveness of our proposed framework and further experiments are conducted to understand how emotion on reviews works for the proposed framework.
AB - Review history is widely used by recommender systems to infer users' preferences and help find the potential interests from the huge volumes of data, whereas it also brings in great concerns on the sparsity and cold-start problems due to its inadequacy. Psychology and sociology research has shown that emotion information is a strong indicator for users' preferences. Meanwhile, with the fast development of online services, users are willing to express their emotion on others' reviews, which makes the emotion information pervasively available. Besides, recent research shows that the number of emotion on reviews is always much larger than the number of reviews. Therefore incorporating emotion on reviews may help to alleviate the data sparsity and cold-start problems for recommender systems. In this paper, we provide a principled and mathematical way to exploit both positive and negative emotion on reviews, and propose a novel framework MIRROR, exploiting eMotIon on Reviews for RecOmmendeR systems from both global and local perspectives. Empirical results on real-world datasets demonstrate the effectiveness of our proposed framework and further experiments are conducted to understand how emotion on reviews works for the proposed framework.
UR - http://www.scopus.com/inward/record.url?scp=85059979892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059979892&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 3788
EP - 3795
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI press
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Y2 - 2 February 2018 through 7 February 2018
ER -