Volume : 5, Issue : 3, MAR 2019
MOVIE RECOMMENDATION ENGINE USING COLLABORATIVE FILTERING WITH ALTERNATIVE LEAST SQUARE AND SINGULAR VALUE DECOMPOSITION ALGORITHMS
ROHAN MHETRE, DR.PRIYA G
Recommender system is a process or approach used for filtering information from a vast dataset and predicting the choices to the users in the areas they are mostly interested in. This system nowadays is the backbone for the commercial aspect of an industry and is established in a variety of areas including movies, music, videos, web-pages, e-commerce, services etc. In this paper the focus is on movie recommendation and the technique currently present for this is the collaborative filtering technique. Of the collaborative filtering techniques, the matrix factorization algorithms namely Alternative Least Square and Singular Value Decomposition are implemented to predict or recommend the movies. Further to improve the processing and time computation for a large dataset we have used Apache Spark along with Elastic search and the accuracy is compared between the two algorithms for different values of testing subsets.
COLLABORATIVE FILTERING, ALS, SVD, APACHE SPARK, ELASTIC SEARCH.
Article : Download PDF
Cite This Article
Article No : 8
Number of Downloads : 36
1. F.O. Isinkaye, Y.O. Folajimi – “Recommendation systems: Principles, methods andevaluation”, Egyptian Informatics Journal– 2015 2. Robin van Meteren, Maarten van Someren– “Using Content-Based Filtering for Recommendation”, NetlinQ Group- 2000 3. Muh Hanafi, N. Suryana – “Paper survey and example of collaborative filtering implementation in recommender system”, Journal of Theoretical and Applied Information Technology– 2017 4. Howal S., Mote A., Vanjari R., Desai V. - “Movie Recommender Engine Using Collaborative Filtering”. 46th ISTE National Convention and National Conference Journal of Advance Research Innovation – 2017 5. Mishra D. P., Mukharjee S., Mahapatra S., Mehta A. – “Analysis of Movie Recommender System using Collaborative Filtering”, International Journal of Recent Trends in Engineering and Research - 2017 6. JonghoonChun, Sang-goo Lee – “A Preprocessing Method for ImprovingEffectiveness of Collaborative Filtering”, ResearchGate - 2003 7. George L., Caravelas P. – “A hybrid approach for movie recommendation”, Springer Science + Business Media, LLC - 2006 8. Hande R., Gutti A., Shah K., Gandhi J., Kamtikar V. –Moviemender-A MovieRecommender System”, International Journal of Engineering Sciences and Research Technology – 2016 9. Lakshmi T. P., Sreenivasa D. P., Siva N. N., Srikanth Y. - “Movie Recommender system using item based collaborative filtering technique”, IEEE – 2016 10. Patel H. M., Shah J. B. – “Collaborative Filtering Approaches for Movie Recommendation System Using Probabilistic Relational Model”, International Journal of Advance research and Development - 2015 11. Zehra C., Mahiye U.– “Feature selection for movie recommendation”, Turkish Journal of Electrical Engineering & Computer Sciences- 2016 12. Ashwani Kumar Singh, P. BeaulahSoundarabai – “International Journal of Advanced Research in Computer and Communication Engineering- 2017 13. Peng, L., Yamada S. – “A Movie Recommender System Based on Inductive Learning”, Conference on Cybernetics and Intelligent Systems Singapore, IEEE – 2004 14. Verma O.P., Katarya R. - “An Effective Collaborative Movie Recommender System with Cuckoo Search”, Egyptian Informatics Journal - 2017 15. Bhumika Bhatt, Premal J Patel. – “A Review Paper on Machine Learning Based Recommendation System”, International Journal of Engineering Development and Research- 2014 16. Robert Bell, Yehuda Koren – “Matrix Factorization Techniques for Recommender Systems”, Cover Feature- 2009 17. Lili Zhao, Zhongqi Lu – “Matrix Factorization+ for Movie Recommendation”, International Joint Conference on Artificial Intelligence - 2016 18. BalazsHidasi, DomonkosTikk– “Speeding up ALS learning via approximate methods for context-aware recommendations”, Springer- 2016 19. Youchun Ji, Wenxing Hong– “Regularized singular value decomposition in news recommendation system”, 2016 11th International Conference on Computer Science & Education (ICCSE) - 2016 20. Spark Programming Guide-Spark 1.6.0 Documentation,http://spark.apache.org/docs/latest/programming-guide.html 21. http://grouplens.org/datasets/movielens/10m/