AN APPROACH FOR IMPLEMENTING A MULTIDIMENSIONAL RECOMMENDATION SYSTEM FOR ONLINE RETAIL SERVICES OR PORTALS

Authors

  • Prof. Nalini A. Mehtre Department of Computer Engineering, SPPU University SKNCOE, Pune-411041, India
  • Meghraj Falle Department of Computer Engineering, SPPU University SKNCOE, Pune-411041, India
  • Praveen Shukla Department of Computer Engineering, SPPU University SKNCOE, Pune-411041, India
  • Saurabh Chaudhari Department of Computer Engineering, SPPU University SKNCOE, Pune-411041, India
  • Aditya Sawant Department of Computer Engineering, SPPU University SKNCOE, Pune-411041, India

Keywords:

Data mining, Multidimensional recommendation system, Performance Analysis, PID algorithm, Product Equivalence Value, Self-Tuning

Abstract

A newly proposed algorithmic approach that can be deployed in the  implementation of recommendation system which can be used at the  client as well as server end. PID is a closed loop, self-tuning algorithm which is predominantly implemented in mechatronics instruments where manual supervision is not feasible and it functions for corrections of errors quantifiable in physical measures. In this approach, we propose a similar outlook to an algorithm for implementation of recommendation system. There exist no physical errors in computer based systems as such but we can abstractly map this error to irrelevancy. If we were to map errors in physical dimensions to irrelevancy in computer systems it can be approximately stated to be higher the error, higher the irrelevancy. Also, the availability of multiple factor assessment in PID algorithms can be used to add multidimensional approach in implementation. As proposed and analysed the implementation is expected to provide with faster as well as large data compliant analytics option.

References

I. Bo-Yang Xing, Li-Ye Yu and Zhong-Kai Zhou, “Composite single neural PID controller based on fuzzy self-tuning gain and RBF network identification”, in Control and Decision Conference (2014 CCDC), 2014.

II. Mehmet Korkmaz, mer Aydodu and Hseyin Doan, “Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller", Innovations in Intelligent Systems and Applications (INISTA) 2012 International Symposium on, pp. 1-5, 2012.

III. Kiam Heong Ang, Gregory Chong and Yun Li, “PID Control System Analysis, Design and Technology", IEEE Transactions On Control Systems Technology”, Vol 13, No.4, July 2005.

IV. Linden, G., Smith, B. and York J: “Amazon.com recommendations: Item-to-item collaborative filtering", Industry Report-IEEE Internet Computing, Jan - Feb 2003, 7(1): pages 76 80.

V. Fabiana Lorenzi and Francesco Ricci, “Case-Based Recommender Systems: a Unifying View", proceedings from IJCAI 2003 Workshop, ITWP-2003, Acapulco, Mexico.

VI. Tamas Jambor, Jun Wang and Neal Lathia, “Using Control Theory For Stable and Efficient Recommender Systems”,in Proceedings 21st international conference on World Wide Web, Pages 11-20, ACM New York, NY,USA 2012, ISBN: 978-1-4503-1229-5

Additional Files

Published

15-03-2017

How to Cite

Prof. Nalini A. Mehtre, Meghraj Falle, Praveen Shukla, Saurabh Chaudhari, & Aditya Sawant. (2017). AN APPROACH FOR IMPLEMENTING A MULTIDIMENSIONAL RECOMMENDATION SYSTEM FOR ONLINE RETAIL SERVICES OR PORTALS. International Education and Research Journal (IERJ), 3(3). Retrieved from https://ierj.in/journal/index.php/ierj/article/view/722

Issue

Vol. 3 No. 3 (2017): MARCH ISSUE, INTERNATIONAL EDUCATION AND RESEARCH JOURNAL

Section

Articles

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Copyright (c) 2021 International Education and Research Journal (IERJ)

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