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CSpace
Algorithms of Unconstrained Non-Negative Latent Factor Analysis for Recommender Systems
Luo, Xin1,2,3; Zhou, Mengchu4; Li, Shuai5; Wu, Di2,3; Liu, Zhigang2,3; Shang, Mingsheng2,3
2021-03-01
摘要Non-negativity is vital for a latent factor (LF)-based model to preserve the important feature of a high-dimensional and sparse (HiDS) matrix in recommender systems, i.e., none of its entries is negative. Current non-negative models rely on constraints-combined training schemes. However, they lack flexibility, scalability, or compatibility with general training schemes. This work aims to perform unconstrained non-negative latent factor analysis (UNLFA) on HiDS matrices. To do so, we innovatively transfer the non-negativity constraints from the decision parameters to the output LFs, and connect them through a single-element-dependent mapping function. Then we theoretically prove that by making a mapping function fulfill specific conditions, the resultant model is able to represent the original one precisely. We subsequently design highly efficient UNLFA algorithms for recommender systems. Experimental results on four industrial-size HiDS matrices demonstrate that compared with four state-of-the-art non-negative models, a UNLFA-based model obtains advantage in prediction accuracy for missing data and computational efficiency. Moreover, such high performance is achieved through its unconstrained training process which is compatible with various general training schemes, on the premise of fulfilling non-negativity constraints. Hence, UNLFA algorithms are highly valuable for industrial applications with the need of performing non-negative latent factor analysis on HiDS matrices.
关键词Data models Training Sparse matrices Recommender systems Computational modeling Big Data Scalability Non-negative latent factor analysis non-negativity latent factor analysis unconstrained optimization high-dimensional and sparse matrix collaborative filtering recommender system big data
DOI10.1109/TBDATA.2019.2916868
发表期刊IEEE TRANSACTIONS ON BIG DATA
ISSN2332-7790
卷号7期号:1页码:227-240
通讯作者Luo, Xin(luoxin21@gmail.com) ; Shang, Mingsheng(msshang@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000626322200018
语种英语