Fractional stochastic gradient descent for recommender systems-Reference-Cited by-同舟云学术

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Fractional stochastic gradient descent for recommender systems

Published:2018-05-30 Issue:2 Volume:29 Page:275-285
ISSN:1019-6781
Container-title:Electronic Markets
language:en
Short-container-title:Electron Markets

Author:

Khan Zeshan Aslam,Chaudhary Naveed Ishtiaq,Zubair Syed

Publisher

Springer Science and Business Media LLC

Subject

Management of Technology and Innovation,Marketing,Computer Science Applications,Economics and Econometrics,Business and International Management

Link

http://link.springer.com/article/10.1007/s12525-018-0297-2/fulltext.html

Reference72 articles.

1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state- of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

2. Aggarwal, C. C. (2016). An Introduction to Recommender Systems. In Recommender Systems (pp. 1–28). Springer International Publishing. https://doi.org/10.1007/978-3-319-29659-3_1 .

3. Aggarwal, C. C., & Parthasarathy, S. (2001). Mining massively incomplete data sets by conceptual reconstruction. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 227–232). ACM.

4. Aslam, M. S., & Raja, M. A. Z. (2015). A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Processing, 107, 433–443.

5. Aslam, M. S., Chaudhary, N. I., & Raja, M. A. Z. (2017). A sliding-window approximation-based fractional adaptive strategy for Hammerstein nonlinear ARMAX systems. Nonlinear Dynamics, 87(1), 519–533.

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