Nearby Product Recommendation System Based on Users Rating

Authors

  • Akanksha Jyoti  Department of Computer Science & Engineering, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Abhijeet Roy  Department of Computer Science & Engineering, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Suraj Singh  Department of Computer Science & Engineering, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Nabab Shaikh  Department of Computer Science & Engineering, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Payal Desai  Assistant Professor, Department of Computer Science & Engineering, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT1952268

Keywords:

E-Commerce, Google Map, Nearby Store, Recommendation System, Self-Service, Technology

Abstract

The recommendation system is very popular nowadays. Recommendation system emerged over the last decade for better findings of things over the internet. Most websites use a recommendation system for tracking and finding items by the user's behavior and preferences. Netflix, Amazon, LinkedIn, Pandora etc. platform gets 60%-70% views results from recommendation. The purpose of this paper is to introduce a recommendation system for local stores where the user gets a nearby relevant recommended item based on the rating of other local users. There are various types of recommendation systems one is User-based collaborative filtering by which the system built upon and uses user's past behavior like ratings and gives similar results made by another user. In collaborative filtering uses Euclidean distance algorithm is used to find the user's rate score to make relations with other users and Euclidean distance similarity score distinguish similarity between users. K-nearest neighbor algorithm is used to implement and find the number of users like new user where K is several similar users. Integrate with map interface to find shortest distances among stores whose product are recommended. The dataset of JSON is used to parse through the algorithm. The result shows a better approach towards the recommendation of products among local stores within a region.

References

  1. An A, Fang. Research commerce personalized recommendation service information. University of International Business and Economics.2006.
  2. George Prassas, Katherine C. Pramataris, 14th Bled Electronic Commerce Conference Bled, Slovenia, June 25 - 26, 2001.
  3. Lohse, P.L and Spiller, P. Electronic Shopping. Communications of the ACM. Vol. 41(7). 1998.
  4. Nichols, D.M. Implicit Rating and Filtering. In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering,10-12. Budapaest, Hungary, ERCIM, 1997.
  5. Maltz, D. and Ehrlich, K. Pointing the Way: Active Collaborative Filtering. In Proceedings CHI'95, 202-209. 1995.
  6. Anuj Jain 2012 MapMyIndia API https://www.mapmyindia.com/api/ accessed on 12th March 2019.
  7. Maltz, D.A. Distributing Information for Collaborative Filtering on Usenet Net News. MIT Department of EECS MS Thesis. May 1994.
  8. Kevin Liao 2018 https://towardsdatascience.com/prototyping-a-recommender-system-step-by-step-part-1-knn-item-based-collaborative-filtering-637969614ea accessed on 12th March 2019.
  9. Breese J, Hecherman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. 1998. 43~52.
  10. Anton, Howard (1994), Elementary Linear Algebra (7th ed.), John Wiley & Sons, pp. 170–171, ISBN 978-0-471-58742-2
  11. Shakhnarovish, Darrell, and Indyk, eds. (2005). Nearest-Neighbor Methods in Learning and Vision. MIT Press. ISBN 978-0-262-19547-8.
  12. Maurits van der Goes 2017 https://neo4j.com/blog/collaborative-filtering-creating-teams/ accessed on 12th March 2017.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-04-30

Issue

Volume 5, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Akanksha Jyoti, Abhijeet Roy, Suraj Singh, Nabab Shaikh, Payal Desai, " Nearby Product Recommendation System Based on Users Rating , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.963-968, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT1952268