Fractional stochastic gradient descent for recommender systems

Fractional stochastic gradient descent for recommender systems Recently, recommender systems are getting popular in the e-commerce industry for retrieving and recommending most relevant information about items for users from large amounts of data. Different stochastic gradient descent (SGD) based adaptive strategies have been proposed to make recommendations more precise and efficient. In this paper, we propose a fractional variant of the standard SGD, named as fractional stochastic gradient descent (FSGD), for recommender systems. We compare its convergence and estimated accuracy with standard SGD against a number of features with different learning rates and fractional orders. The performance of our proposed method is evaluated using the root mean square error (RMSE) as a quantitative evaluation measure. We examine that the proposed strategy is more accurate in terms of RMSE than the standard SGD for all values of fractional orders and different numbers of features. The contribution of fractional calculus has not been explored yet to solve the recommender systems problem; therefore, we exploit FSGD for solving this problem. The results show that our proposed method performs significantly well in terms of estimated accuracy and convergence as compared to the standard SGD. . . . Keywords Recommender systems E-Commerce Fractional calculus Stochastic gradient descent Introduction A recommender system http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Electronic Markets Springer Journals

Fractional stochastic gradient descent for recommender systems

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Institute of Applied Informatics at University of Leipzig
Subject
Business and Management; IT in Business; e-Commerce/e-business
ISSN
1019-6781
eISSN
1422-8890
D.O.I.
10.1007/s12525-018-0297-2
Publisher site
See Article on Publisher Site

Abstract

Recently, recommender systems are getting popular in the e-commerce industry for retrieving and recommending most relevant information about items for users from large amounts of data. Different stochastic gradient descent (SGD) based adaptive strategies have been proposed to make recommendations more precise and efficient. In this paper, we propose a fractional variant of the standard SGD, named as fractional stochastic gradient descent (FSGD), for recommender systems. We compare its convergence and estimated accuracy with standard SGD against a number of features with different learning rates and fractional orders. The performance of our proposed method is evaluated using the root mean square error (RMSE) as a quantitative evaluation measure. We examine that the proposed strategy is more accurate in terms of RMSE than the standard SGD for all values of fractional orders and different numbers of features. The contribution of fractional calculus has not been explored yet to solve the recommender systems problem; therefore, we exploit FSGD for solving this problem. The results show that our proposed method performs significantly well in terms of estimated accuracy and convergence as compared to the standard SGD. . . . Keywords Recommender systems E-Commerce Fractional calculus Stochastic gradient descent Introduction A recommender system

Journal

Electronic MarketsSpringer Journals

Published: May 30, 2018

References

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