Journal
NEURAL COMPUTING & APPLICATIONS
Volume 32, Issue 14, Pages 10245-10262Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-019-04562-6
Keywords
Recommender systems; Normalized adaptive algorithms; E-commerce; Fractional calculus; Stochastic gradient descent
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Funding
- Ministry of Education, Science and Technological Development of the Republic of Serbia [TR-31046]
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Fast and effective recommender systems are fundamental to fulfill the growing requirements of the e-commerce industry. The strength of matrix factorization procedure based on stochastic gradient descent (SGD) algorithm is exploited widely to solve the recommender system problem. Modern computing paradigms are designed by utilizing the concept of fractional gradient in standard SGD and outperform the standard counterpart. The performance of fractional SGD improves considerably by adaptively tuning the learning rate parameter. A nonlinear computing paradigm based on normalized version of fractional SGD is developed in this paper to investigate the adaptive behavior of learning rate with novel application to recommender systems. The accuracy of the proposed approach is verified through root mean square error metric by using different latent features, learning rates, fractional orders and datasets. The superiority of the designed method is validated through comparison with the state-of-the-art counterparts.
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