Product Quantization using Regression
Vasanthi Vadlamudi1, Divya Sree Ravi2, Rishita Dhulipalla3, Rohith Vamsi Danduboyina4, K.S. Vijaya Lakshmi5

1Vasanthi Vadlamudi, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
2Divya Sree Ravi, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
3Rishita Dhulipalla, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
4Rohith Vamsi Danduboyina, Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
5K.S.Vijaya Lakshmi, Assistant professor at the Department of Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, Kanuru.
Manuscript received on May 16, 2020. | Revised Manuscript received on May 30, 2020. | Manuscript published on June 10, 2020. | PP: 653-655 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6483069820/2020©BEIESP | DOI: 10.35940/ijitee.H6483.069820
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Approximate Nearest Neighbor (ANN) has developed an immense demand for many tasks. This ANN methodology was being used for product quantization. These product quantization methods were being used for e-commerce sites. However, this quantization maybe sometimes misleading due to a lack of accuracy in technique. So, we managed to increase the accuracy of quantization by adding Logistic Regression in the process. This helps to increase the accuracy of the method by having a probability value. This helps to make correlated items much more accurate when compared to pure quantization. This method is helpful for e-commerce sites for efficiency in the prediction of purchase by the customer. .
Keywords: Approximate nearest neighbor, Product quantization, Quantization, Regression.
Scope of the Article: Software Engineering Techniques and Production Perspectives