Movie Recommended System by Using Collaborative Filtering

Authors

  • Bheema Shireesha  Assistant Professor, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India
  • Navuluri Madhavilatha  Guest Faculty, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India
  • Chunduru Anilkumar  Assistant Professor, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/CSEIT19511

Keywords:

Hadoop, Collaborative filtering, Machine Learning, HDFS, RSSI

Abstract

Recommendation system helps people in decision making an item/person. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. In this project, we attempt to under- stand the different kinds of recommendation systems and compare their performance on the Movie Lens dataset. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem.

References

  1. A Survey of Collaborative Filtering Techniques;https://www.hindawi.com/journals/aai/2009/421425/.,2017, Vol. 13(7).
  2. Google News Personalization: Scalable Online Collaborative Filtering; Das et al; https://www2007.org/papers/paper570.pdf
  3. Intro to Recommender Systems: Collaborative Filtering;http://blog.ethanrosenthal.com/2015/11/02/intro-to-collaborative -filtering/"
  4. Zhan J, Hsieh CL, Wang IC, Hsu TS, Liau CJ, Wang DW. Privacy-preserving collaborative recommender systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
  5. Gong S. A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software. 2010; 5(7):745-52.
  6. Manvi SS, Nalini N, Bhajantri LB. Recommender system in ubiquitous commerce. In international conference on electronics computer technology 2011 (pp. 434-8). IEEE.
  7. Pu P, Chen L, Hu R. A user-centric evaluation framework for recommender systems. In proceedings of the fifth ACM conference on recommender systems 2011 (pp. 157-64). ACM.
  8. Hu R, Pu P. Acceptance issues of personality-based recommender systems. In proceedings of the third ACM conference on recommender systems 2009 (pp. 221-4).ACM."
  9. Pathak B, Garfinkel R, Gopal RD, Venkatesan R, Yin F. Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems. 2010; 27(2):159-88. " ,
  10. Witten IH, Frank E, Hall MA. Data mining: practical machine leaning toots and techniques. Morgan Kaufmann Publishers, Elsevier; 2011."

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

Downloads

Published

2019-01-30

Issue

Volume 5, Issue 1, January-February-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]
Bheema Shireesha, Navuluri Madhavilatha, Chunduru Anilkumar, " Movie Recommended System by Using Collaborative Filtering" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.09-15, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT19511