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Movie Recommendation based on Users' Tweets

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
G. Hemantha Kumar, Seyedmahmoud Talebi

Hemantha G Kumar and Seyedmahmoud Talebi. Movie Recommendation based on Users' Tweets. International Journal of Computer Applications 141(14):34-36, May 2016. BibTeX

	author = {G. Hemantha Kumar and Seyedmahmoud Talebi},
	title = {Movie Recommendation based on Users' Tweets},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {14},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {34-36},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2016909992},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper new idea for recommending movies has been designed. This system is based on machine learning algorithm which calculate similarity between user's tweets and scenario of the movies. This method could recommend movies which are more similar to users' interest.


  1. Lekakos G, Caravelas P. A hybrid approach for movie recommendation. Multimedia tools and applications. 2008;36(1–2):55–70.
  2. Jafarkarimi H, Sim ATH, Saadatdoost R. A naive recommendation model for large databases. International Journal of Information and Education Technology. 2012;2(3):216.
  3. Lee D, Lee S-K, Lee S. Considering temporal context in music recommendation based on collaborative filtering. In: Proceedings of Korea computer congress. 2009.
  4. Melville P, Sindhwani V. Encyclopedia of machine learning. Springer-Verlag, chapter Recommender systems; 2010.
  5. Lee H. Enhancement of Collaborative Filtering in Electronic Commerce Recommender System. Kangwon University Graduation School. Doctoral thesis; 2009.
  6. Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. Springer; 2011.
  7. Eyjolfsdottir EA, Tilak G, Li N. Moviegen: A movie recommendation system. UC Santa Barbara: Technical Report. 2010;
  8. Lawrence RD, Almasi GS, Kotlyar V, Viveros M, Duri SS. Personalization of supermarket product recommendations. Springer; 2001.
  9. Jung S, Harris K, Webster J, Herlocker JL. SERF: integrating human recommendations with search. In: Proceedings of the thirteenth ACM international conference on Information and knowledge management. ACM; 2004. p. 571–580.
  10. Berry MW, Kogan J. Text Mining. Applications and Theory West Sussex, PO19 8SQ, UK: John Wiley & Sons. 2010;
  11. Brusilovski P, Kobsa A, Nejdl W. The adaptive web: methods and strategies of web personalization. Vol. 4321. Springer Science & Business Media; 2007.


Movie Recommendation, recommender System,Text Similarity