TY - JOUR
T1 - An Efficient Second-Order Approach to Factorize Sparse Matrices in Recommender Systems
AU - Luo, Xin
AU - Zhou, Mengchu
AU - Li, Shuai
AU - Xia, Yunni
AU - You, Zhuhong
AU - Zhu, Qingsheng
AU - Leung, Hareton
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2015/8/1
Y1 - 2015/8/1
N2 - Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders.
AB - Recommender systems are an important kind of learning systems, which can be achieved by latent-factor (LF)-based collaborative filtering (CF) with high efficiency and scalability. LF-based CF models rely on an optimization process with respect to some desired latent features; however, most of them employ first-order optimization algorithms, e.g., gradient decent schemes, to conduct their optimization task, thereby failing in discovering patterns reflected by higher order information. This work proposes to build a new LF-based CF model via second-order optimization to achieve higher accuracy. We first investigate a Hessian-free optimization framework, and employ its principle to avoid direct usage of the Hessian matrix by computing its product with an arbitrary vector. We then propose the Hessian-free optimization-based LF model, which is able to extract latent factors from the given incomplete matrices via a second-order optimization process. Compared with LF models based on first-order optimization algorithms, experimental results on two industrial datasets show that the proposed one can offer higher prediction accuracy with reasonable computational efficiency. Hence, it is a promising model for implementing high-performance recommenders.
KW - Collaborative-filtering
KW - Hessian-free Optimization
KW - Incomplete Matrices
KW - Latent Factor Model
KW - Recommender Systems
KW - Second-order Optimization
UR - http://www.scopus.com/inward/record.url?scp=84938598026&partnerID=8YFLogxK
U2 - 10.1109/TII.2015.2443723
DO - 10.1109/TII.2015.2443723
M3 - 文章
AN - SCOPUS:84938598026
SN - 1551-3203
VL - 11
SP - 946
EP - 956
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 4
M1 - 7120953
ER -