TY - GEN
T1 - Factorized Similarity Learning in Networks
AU - Chang, Shiyu
AU - Qi, Guo Jun
AU - Aggarwal, Charu C.
AU - Zhou, Jiayu
AU - Wang, Meng
AU - Huang, Thomas S.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such as Sim Rank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a Factorized Similarity Learning (FSL) is proposed to integrate the link, node content, and user supervision into an uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge-loss alternatively. To facilitate efficient computation on large scale data, a parallel extension is developed. Experiments are conducted on the DBLP and CoRA datasets. The results show that FSL is robust, efficient, and outperforms the state-of-the-art.
AB - The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such as Sim Rank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a Factorized Similarity Learning (FSL) is proposed to integrate the link, node content, and user supervision into an uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge-loss alternatively. To facilitate efficient computation on large scale data, a parallel extension is developed. Experiments are conducted on the DBLP and CoRA datasets. The results show that FSL is robust, efficient, and outperforms the state-of-the-art.
KW - Content
KW - Link
KW - Network similarity
KW - Supervised matrix factorization
KW - Supervision
UR - http://www.scopus.com/inward/record.url?scp=84936933121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936933121&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2014.115
DO - 10.1109/ICDM.2014.115
M3 - Conference contribution
AN - SCOPUS:84936933121
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 60
EP - 69
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
Y2 - 14 December 2014 through 17 December 2014
ER -