Personalized Movie Recommendation System Using Twitter Data

  • Debashis Das
  • Himadri Tanaya Chidananda
  • Laxman Sahoo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)


Nowadays, we are living in an age recommendation, but the proper recommendation needs more accurate and relevant datas as their inputs. Rating databases like MovieLence or Netflix have long been popular and being widely used in recommendation system areas for research in past decades. But nowadays, they become irrelevant due to lack of new and relevant datas. Nowadays, social media like Facebook and Twitter become the most popular for researchers due to availability of large amount of new and relevant datas. In this paper, we have built a recommendation engine by analyzing rating datasets collected from Twitter to recommend movies to specific user using R.


Recommendation system Type of recommendation system Item-based collaborating filtering 



We are thankful to the faculty members of School of Computer Engineering Department of KIIT University, Bhubaneswar, for their cooperation and suggestions.


  1. 1.
  2. 2.
  3. 3.
    J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68–73.Google Scholar
  4. 4.
    Halder, Sajal, et al. “Movie swarm: Information mining technique for movie recommendation system.” Electrical & Computer Engineering (ICECE), 2012 7th International Conference on. IEEE, 2012.Google Scholar
  5. 5.
    Debashis Das, Laxman Sahoo and Sujoy Datta. A Survey on Recommendation System. International Journal of Computer Applications 160(7): 6–10, February 2017.Google Scholar
  6. 6.
    Yang, Wei, et al. “User’s Interests-Based Movie Recommendation in Heterogeneous Network.” Identification, Information, and Knowledge in the Internet of Things (IIKI), 2015 International Conference on. IEEE, 2015.Google Scholar
  7. 7.
    Kim, RyuRi, et al. “Trustworthy Movie Recommender System with Correct Assessment and Emotion Evaluation.” Proceedings of the International MultiConference of Engineers and Computer Scientists. Vol. 2. 2015.Google Scholar
  8. 8.
    Oliveira, Eva, Nuno Ribeiro, and Teresa Chambel. “Accessing movies’ emotional information.” Information Systems and Technologies (CISTI), 2015 10th Iberian Conference on. IEEE, 2015.Google Scholar
  9. 9.
    Sneha Khatwani, Dr. M.B. Chandak. “Building Personalized and Non Personalized Recommendation Systems.” International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), 2016 International Institute of Information Technology (I2IT), Pune.Google Scholar
  10. 10.
    Dooms, Simon, Toon De Pessemier, and Luc Martens. “Movietweetings: a movie rating dataset collected from twitter.” Workshop on Crowdsourcing and human computation for recommender systems, CrowdRec at RecSys. Vol. 2013. 2013.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Debashis Das
    • 1
  • Himadri Tanaya Chidananda
    • 1
  • Laxman Sahoo
    • 1
  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia

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