A Facebook Profile Based TV Shows and Movies Recommendation System

Authors

  • Prof S.R Dhore Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Abhishek Shukla Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Anil Kumar Pal Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Manish Kumar Department of Computer Engineering, Army Institute of Technology, Pune, India

Keywords:

Recommendation System, Collaborative Filtering, Content Filtering, Naive Bayes, Information Retrieval, Graph Theory.

Abstract

Implemented and evaluated different algorithms in the context of developing a recommendation system
based on data gathered from Facebook user profiles. In particular, we are looking at a Collaborative Filtering
algorithms, a Content Filtering approach, and Naive Bayes, and comparing their performance in terms of standard
measures. The algorithms draw from principles and techniques in Machine Learning, Information Retrieval, as well as
Graph Theory. The Facebook graph API was used to scrape friend’s Facebook profile data. This results in a dataset of
Facebook user profiles in XML format, listing different attributes for a particular user. The ’liked’ TV show and movies
sections act as the labels for our training and test data , and the rest of the sections are used as the supporting attributes.

Published

2017-03-25

How to Cite

Prof S.R Dhore, Abhishek Shukla, Anil Kumar Pal, & Manish Kumar. (2017). A Facebook Profile Based TV Shows and Movies Recommendation System. International Journal of Advance Engineering and Research Development (IJAERD), 4(3), 636–640. Retrieved from https://ijaerd.com/index.php/IJAERD/article/view/2141