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The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen

Curnalia, James W.
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838

Abstract Details

2013, Master of Computing and Information Systems, Youngstown State University, Department of Computer Science and Information Systems.
More and more outlets are utilizing collaborative filtering techniques to make sense of the sea of data generated by our hyper-connected world. How a collaborative filtering model is generated can be the difference between accurate or flawed predictions. This study is to determine the impact of a cyclical training regimen on the algorithms presented in the Collaborative Filtering Toolkit for GraphChi. Initial testing shows that some of the algorithms benefited from a multi-cyclic approach, a result that is reinforced by repeating the experiment on a separate dataset. Additional testing focuses on the effectiveness of dynamic versus fixed training cycle sizes. However, there is no additional benefit to adopting this more complex training scheme. While the results are far from universal, half of the algorithms saw a significant increase in accuracy when subjected to a multi-cyclic training regimen.
Alina Lazar, Ph.D. (Advisor)
Bonita Sharif, Ph.D. (Committee Member)
John Sullins, Ph.D. (Committee Member)
44 p.
Artificial Intelligence; Computer Science; Statistics
collaborative filtering; recommendation; model; training; machine learning; algorithms

Recommended Citations

Citations

  • Curnalia, J. W. (2013). The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838

    APA Style (7th edition)

  • Curnalia, James. The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen. 2013. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838.

    MLA Style (8th edition)

  • Curnalia, James. "The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen." Master's thesis, Youngstown State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838

    Chicago Manual of Style (17th edition)

ysu1370016838
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© 2013, all rights reserved.
This open access ETD is published by Youngstown State University and OhioLINK.