TY - JOUR
T1 - Non-Negative Latent Factor Model Based on β-Divergence for Recommender Systems
AU - Xin, Luo
AU - Yuan, Ye
AU - Zhou, Mengchu
AU - Liu, Zhigang
AU - Shang, Mingsheng
N1 - Funding Information:
Manuscript received June 3, 2019; accepted July 18, 2019. Date of publication August 21, 2019; date of current version July 19, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61772493, Grant 91646114, Grant 51609229, Grant 61872065, and Grant 61702475, in part by the National Key Research and Development Program of China under Grant 2017YFC0804002, in part by the Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group under Grant cstc2017kjrc-cxcytd0149, in part by the Chongqing Overseas Scholars Innovation Program under Grant cx2017012 and Grant cx2018011, in part by the Chongqing Research Program of Technology Innovation and Application under Grant cstc2017zdcy-zdyfX0076, Grant cstc2018jszx-cyztzxX0025, Grant cstc2017rgzn-zdyfX0020, Grant cstc2017zdcy-zdyf0554, and Grant cstc2017rgzn-zdyf0118, and in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences. This paper was recommended by Associate Editor J. A. Lozano. (Xin Luo and Ye Yuan are co-first authors.) (Corresponding authors: MengChu Zhou; Mingsheng Shang.) X. Luo is with the School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China (e-mail: luoxin21@gmail.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF models mostly adopt Euclidean distance in their objective function, which represents a special case of a {{β }} -divergence function. Hence, it is highly desired to design a {{β }} -divergence-based NLF ( {{β }} -NLF) model that uses a {{β }} -divergence function, and investigate its performance in recommender systems as {{β }} varies. To do so, we first model {{β }} -NLF's learning objective with a {{β }} -divergence function. Subsequently, we deduce a general single latent factor-dependent, non-negative and multiplicative update scheme for {{β }} -NLF, and then design an efficient {{β }} -NLF algorithm. The experimental results on HiDS matrices from industrial applications indicate that by carefully choosing the value of {{β }} , {{β }} -NLF outperforms an NLF model with Euclidean distance in terms of accuracy for missing data prediction without increasing computational time. The research outcomes show the necessity of using an optimal {{β }} -divergence function in order to achieve the best performance of an NLF model on HiDS matrices. Hence, the proposed model has both theoretical and application significance.
AB - Non-negative latent factor (NLF) models well represent high-dimensional and sparse (HiDS) matrices filled with non-negative data, which are frequently encountered in industrial applications like recommender systems. However, current NLF models mostly adopt Euclidean distance in their objective function, which represents a special case of a {{β }} -divergence function. Hence, it is highly desired to design a {{β }} -divergence-based NLF ( {{β }} -NLF) model that uses a {{β }} -divergence function, and investigate its performance in recommender systems as {{β }} varies. To do so, we first model {{β }} -NLF's learning objective with a {{β }} -divergence function. Subsequently, we deduce a general single latent factor-dependent, non-negative and multiplicative update scheme for {{β }} -NLF, and then design an efficient {{β }} -NLF algorithm. The experimental results on HiDS matrices from industrial applications indicate that by carefully choosing the value of {{β }} , {{β }} -NLF outperforms an NLF model with Euclidean distance in terms of accuracy for missing data prediction without increasing computational time. The research outcomes show the necessity of using an optimal {{β }} -divergence function in order to achieve the best performance of an NLF model on HiDS matrices. Hence, the proposed model has both theoretical and application significance.
KW - big data
KW - high-dimensional and sparse (HiDS) matrix
KW - industrial application
KW - learning algorithm
KW - non-negative latent factor (NLF) analysis
KW - recommender system
KW - β-divergence
UR - http://www.scopus.com/inward/record.url?scp=85110447998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85110447998&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2019.2931468
DO - 10.1109/TSMC.2019.2931468
M3 - Article
AN - SCOPUS:85110447998
SN - 2168-2216
VL - 51
SP - 4612
EP - 4623
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 8
M1 - 8809405
ER -