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Reseach Article

About Performance Evaluation of the Movie Recommendation Systems

by Shreya Agrawal, Pooja Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 158 - Number 2
Year of Publication: 2017
Authors: Shreya Agrawal, Pooja Jain
10.5120/ijca2017912739

Shreya Agrawal, Pooja Jain . About Performance Evaluation of the Movie Recommendation Systems. International Journal of Computer Applications. 158, 2 ( Jan 2017), 7-10. DOI=10.5120/ijca2017912739

@article{ 10.5120/ijca2017912739,
author = { Shreya Agrawal, Pooja Jain },
title = { About Performance Evaluation of the Movie Recommendation Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 158 },
number = { 2 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume158/number2/26878-2017912739/ },
doi = { 10.5120/ijca2017912739 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:03:44.158024+05:30
%A Shreya Agrawal
%A Pooja Jain
%T About Performance Evaluation of the Movie Recommendation Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 158
%N 2
%P 7-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Movie recommendation systems are now becoming very popular both commercially and also in the research community, where many approaches have been proposed for providing recommendations. For more and more usage of any system, it is necessary to know about the efficiency of the system and for this reason performance evaluation of a Recommendation system is done. By doing the performance evaluation of a system, one can prove the potential of a recommendation system. The more high performance a system gives more is its worth as compared to others. And, on this basis we can get to know further research and improvement options for a system which gives rise to new advancements in the field. Indeed, movie recommendation systems have a number of properties that may affect user’s experience, such as accuracy, quality, robustness, scalability, and so forth. In this paper, various important performance evaluation metrics are reviewed and discussed.

References
  1. Iman Avazpour, Teerat Pitakrat, Lars Grunske and John Grundy (2014), “Dimensions and Metrics for Evaluating Recommendation Systems”, Recommendation Systems in Software Engineering, Part II, pp 245-273, Springer Berlin Heidelberg.
  2. Woon-hae Jeong, Se-jun Kim, Doo-soon Park and Jin Kwak (2013), “Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering”, Journal of Information Processing Systems, Vol. 9, no. 1
  3. Wu, W., He, L., and Yang, J. (2012), “Evaluating Recommender Systems,” 7th International Conference on Digital Information Management (ICDIM), pp.56–61
  4. Mouzhi Ge, Carla Delgado-Battenfeld, Dietmar Jannach (2010), “Beyond Accuracy: Evaluating Recommender Systems by Coverage and Serendipity”, Proceedings of the fourth ACM conference on Recommender systems. ACM, 2010.
  5. Gunawardana, A. and Shani, G. (2009), “A Survey of Accuracy Evaluation Metrics of Recommendation Tasks”, The Journal of Machine Learning Research, 10, p.2935-2962, 12/1/2009.
  6. Francois Fouss & Marco Saerens (2008), “Evaluating performance of recommender systems: An experimental comparison”, IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.
  7. G. Adomavicius and A. Tuzhilin (2005), “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”, IEEE Transactions on Knowledge and Data Engineering, pages 734–749, 2005.
  8. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl (2004), “Evaluating collaborative filtering recommender systems”, ACM Transactions on Information Systems, 22(1):5–53, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Movie recommendation systems performance evaluation accuracy scalability quality