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
Electronic Markets – Springer Journals
Published: May 30, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera