The week's pick
Random Articles
Reseach Article
Collaborative Filtering for Movie Recommendation using RapidMiner
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 169 - Number 6 |
| Year of Publication: 2017 |
| Authors: Arpita Jain, Santosh K. Vishwakarma |
10.5120/ijca2017914771
|
Arpita Jain, Santosh K. Vishwakarma . Collaborative Filtering for Movie Recommendation using RapidMiner. International Journal of Computer Applications. 169, 6 ( Jul 2017), 29-33. DOI=10.5120/ijca2017914771
Abstract
Recommender System is a special type of information filtering system that provides a prediction which helps the user to evaluate items from a huge collection that the user is likely to find interesting or useful. Recommender System is used to produce meaningful suggestions about new items for particular consumers. These recommendations facilitate the users to make decisions in multiple contexts, such as what items to buy, what online news to read or what music to listen to. Recommender Systems have become important in information and decision overloaded in the world. Recommender Systems helped their founders to increase profits. This paper, presents a brief overview of collaborative filtering based movie recommender system and their implementation using rapid miner.
References
- Google: http://www.google.com/.
- Social Computing Research at the University of Minnesota http://www.grouplens.org/.
- D J.Bobadilla, F. Ortega, A.Hernando, A. Gutierrez, Recommender systems survey,Knowledge-Based Systems,46(2013) 109-132.
- P.Resnick,H.R. Varian, Recommender systems, Communications of the ACM, 40(1997) 56-58.
- Deshpande, M., and Karypis, G. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22, 1 (2004), 143-177.
- Breese, J., Heckerman, D., and Kadie, C., Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, page 4352, 1998.
- B.N. Miller, J.A. Konstan, J. Riedl, PocketLens: Toward a personal recommender system, ACM Transactions on Information Systems (TOIS), 22 (2004) 437-476.
- B.N. Miller, J.A. Konstan, J. Riedl, PocketLens: Toward a personal recommender system, ACM Transactions on Information Systems (TOIS), 22 (2004) 437-476.
- Chuangguang Huang and Jian Yin “Effective Association Clusters Filtering to Cold-Start Recommendations”, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery
- J. Salter, N. Antonopoulos, CinemaScreen recommender agent: combining collaborative and content-based filtering, Intelligent Systems, IEEE, 21 (2006) 35-41.
- C. Hayes, P. Cunningham, Smart radio—community based music radio, Knowledge-Based Systems, 14 (2001).
- Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender Systems Handbook. Springer
- Yechun Jiang, Jianxun Liu, Mingdong Tang and Xiaoqing (Frank) Liu “An Effective Web Service Recommendation Method based on Personalized Collaborative Filtering”, 2011 IEEE International Conference on Web Services.
- Guangping Zhuo, Jingyu Sun and Xueli Yu “A Framework for Multi- Type Recommendations”, Eighth International Conference on Fuzzy Systems and Knowledge Discovery, 2007.
- Qian Wang, Xianhu Yuan, Min Sun “Collaborative Filtering Recommendation Algorithm based on Hybrid User Model”, FSKD, 2010.
- Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: a free recommender system library. In Proc. of the fifth ACM conference on Recommender systems, RecSys ’11, pages 305–308, 2011.
Index Terms
Keywords