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A Three Way Hybrid Movie Recommendation Syste

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Karan Soni, Rinky Goyal, Bhagyashree Vadera, Siddhi More

Karan Soni, Rinky Goyal, Bhagyashree Vadera and Siddhi More. A Three Way Hybrid Movie Recommendation Syste. International Journal of Computer Applications 160(9):29-32, February 2017. BibTeX

	author = {Karan Soni and Rinky Goyal and Bhagyashree Vadera and Siddhi More},
	title = {A Three Way Hybrid Movie Recommendation Syste},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {9},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {29-32},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913026},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Recommendation Systems or Engines are found in many applications. These systems or Engines offer the user or service subscriber with a list of suggestions or recommendations that they might choose based on the user’s already known preferences. In this paper, the focus is on combining a content-based algorithm, a User-based collaborative filtering algorithm, and review based text mining algorithm in the application of a tailored movie recommendation system. Here movies are recommended based on ratings explicitly provided by the user and according to the ratings and reviews of movies provided by other users as well. Here the propose is to generate polarity ratings to Characteristics of a movie instead of generating a wholesome rating to an available text based review to gain better insights about preferences of users, thus refining Movie recommendation systems further.


  1. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (August 2009), 30-37. DOI=10.1109/MC.2009.263
  2. D. Pathak, S. Matharia and C. N. S. Murthy, "ORBIT: Hybrid movie recommendation engine," Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN), 2013 International Conference on, Tirunelveli, 2013, pp. 19-24.doi: 10.1109/ICE-CCN.2013.6528589
  3. C. L. Liu, W. H. Hsaio, C. H. Lee, G. C. Lu and E. Jou, "Movie Rating and Review Summarization in Mobile Environment," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 3, pp. 397-407, May 2012.doi: 10.1109/TSMCC.2011.2136334
  4. S. Vinodhini, V. Rajalakshmi, B. Govindarajulu, ”Building Personalized Recommendation System With Big Data and Hadoop MapReduce" ,IJERTV3IS042291 IJERT, April 2014.
  5. Aiming Diao, Minghui Qiu, Chao -Yuan Wu, Alexander J.Smola, Jing Jiang, and ChongWang. 2014.Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '14). ACM, New York, NY, USA, 193-202. DOI:
  6. Hwang, T., Park, C., Hong, J. et al. Multimed Tools Appl (2016) 75: 12843. doi:10.1007/s11042-016-3526-8
  7. Kyung-Yong Jung, Dong-Hyun Park and Jung-Hyun Lee, “Hybrid Collaborative Filtering and Content-Based Filteringfor Improved Recommender System”in Lecture Notes in Computer Science, 2004, Volume 3036/2004, Springer, pp. 295-302
  8. George Lekakos and Petros Caravelas, “A Hybrid Approach for Movie Recommendation” in Multimedia Tools and Applications, Volume 36, Numbers 1-2, January 2008, Springer, pp. 55-70
  9. Robin Burke, “Hybrid Recommender Systems: Survey and Experiments” in User Modeling and User-Adapted Interaction, Volume 12 Issue 4, November 2002, pp. 331-370
  10. Perm Melville, Raymond J. Mooney, Ramadass Nagarajan, “Content-Boosted Collaborative Filtering for Improved Recommendations” in the Proceedings of the 2002 American Association for Artificial Intelligence, pp. 187 – 192
  11. Y. Wang, Y. Liu and X. Yu, "Collaborative Filtering with Aspect-Based Opinion Mining: A Tensor Factorization Approach," 2012 IEEE 12th International Conference on Data Mining, Brussels, 2012, pp. 1152-1157. doi: 10.1109/ICDM.2012.76
  12. Zhenxue Zhang. 2013. Urcf: An Approach to Integrating User Reviews into Memory-Based Collaborative Filtering. Ph.D. Dissertation. University of Maryland at Baltimore County, Catonsville, MD, USA. Advisor(s) Dongsong Zhang. AAI356338.


SVM classification, user based collaborative filtering, Content based filtering