TY - GEN
T1 - Learning dual preferences with non-negative matrix tri-factorization for top-N recommender system
AU - Li, Xiangsheng
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Chen, Yufu
AU - Lau, Raymond Y.K.
AU - Wang, Fu Lee
AU - Yin, Jian
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - In recommender systems, personal characteristic is possessed by not only users but also displaying products. Users have their personal rating patterns while products have different characteristics that attract users. This information can be explicitly exploited from the review text. However, most existing methods only model the review text as a topic preference of products, without considering the perspectives of users and products simultaneously. In this paper, we propose a user-product topic model to capture both user preferences and attractive characteristics of products. Different from conventional collaborative filtering in conjunction with topic models, we use non-negative matrix tri-factorization to jointly reveal the characteristic of users and products. Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations.
AB - In recommender systems, personal characteristic is possessed by not only users but also displaying products. Users have their personal rating patterns while products have different characteristics that attract users. This information can be explicitly exploited from the review text. However, most existing methods only model the review text as a topic preference of products, without considering the perspectives of users and products simultaneously. In this paper, we propose a user-product topic model to capture both user preferences and attractive characteristics of products. Different from conventional collaborative filtering in conjunction with topic models, we use non-negative matrix tri-factorization to jointly reveal the characteristic of users and products. Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations.
KW - Matrix tri-factorization
KW - Top-N recommender system
KW - Topic model
UR - http://www.scopus.com/inward/record.url?scp=85048033422&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91452-7_9
DO - 10.1007/978-3-319-91452-7_9
M3 - Conference contribution
AN - SCOPUS:85048033422
SN - 9783319914510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 149
BT - Database Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
A2 - Manolopoulos, Yannis
A2 - Li, Jianxin
A2 - Sadiq, Shazia
A2 - Pei, Jian
T2 - 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
Y2 - 21 May 2018 through 24 May 2018
ER -