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Comparative Study on Approaches of Recommendation System
by
Roshni K. Sorde,
Sachin N. Deshmukh
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 118 - Number 2 |
| Year of Publication: 2015 |
| Authors: Roshni K. Sorde, Sachin N. Deshmukh |
10.5120/20716-3059
|
Roshni K. Sorde, Sachin N. Deshmukh . Comparative Study on Approaches of Recommendation System. International Journal of Computer Applications. 118, 2 ( May 2015), 10-14. DOI=10.5120/20716-3059
@article{
10.5120/20716-3059,
author = {
Roshni K. Sorde,
Sachin N. Deshmukh
},
title = { Comparative Study on Approaches of Recommendation System },
journal = {
International Journal of Computer Applications
},
issue_date = { May 2015 },
volume = { 118 },
number = { 2 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = {
10-14
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume118/number2/20716-3059/
},
doi = { 10.5120/20716-3059 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:00:34.702759+05:30
%A Roshni K. Sorde
%A Sachin N. Deshmukh
%T Comparative Study on Approaches of Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 2
%P 10-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Recommender systems are the software engines and approaches for providing suggestion of products to the user which might be most probably matched with the user's like/choice. Generally, Recommendation system gives suggestions on content like what items to buy, which music to listen or what online news to read. Depending on the users liking the items are suggested. In this paper we will be discussing about the research done in the Recommendation System its approaches and the techniques used. At the end we will go through the main challenges and the limitation of the system.
References
- Karypis, George. "Evaluation of item-based top-n recommendation algorithms. " Proceedings of the tenth international conference on Information and knowledge management. ACM, 2001.
- Asanov, Daniar. "Algorithms and methods in recommender systems. " Berlin Institute of Technology, Berlin, Germany (2011)
- Survey Paper on Recommendation System Mukta kohar,Chhavi Rana Department of computer science and Engg U. I. E. T, MDU,Rohtak .
- Gunawardana, Asela, and Guy Shani. "A survey of accuracy evaluation metrics of recommendation tasks. " The Journal of Machine Learning Research 10 (2009): 2935-2962.
- Cover, T. ,and Hart, P. , Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21–27, 1967
- Miranda, Tim, et al. "Combining content-based and collaborative filters in an online newspaper. " In Proceedings of ACM SIGIR Workshop on Recommender Systems. 1999.
- Almazro, Dhoha, et al. "A survey paper on recommender systems. " arXiv preprint arXiv:1006. 5278 (2010).
- Pazzani, Michael J. , and Daniel Billsus. "Content-based recommendation systems. " The adaptive web. Springer Berlin Heidelberg, 2007. 325-341.
- Sarwar, Badrul, et al. "Item-based collaborative filtering recommendation algorithms. " Proceedings of the 10th international conference on World Wide Web. ACM, 2001.
- Das, A. S. , Datar, M. , Garg, A. , & Rajaram, S. (2007, May). Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web (pp. 271-280). ACM.
- Van Meteren, Robin, and Maarten Van Someren. "Using content-based filtering for recommendation. " Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop. 2000.
- Pazzani, Michael J. "A framework for collaborative, content-based and demographic filtering. " Artificial Intelligence Review 13. 5-6 (1999): 393-408.
- Melville, Prem, and Vikas Sindhwani. "Recommender systems. " Encyclopedia of machine learning. Springer US, 2010. 829-838.
- Van Meteren, Robin, and Maarten Van Someren. "Using content-based filtering for recommendation. " Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop. 2000.
- ] Kuppusamy, K. S. , and G. Aghila. "A personalized web page content filtering model based on segmentation. " arXiv preprint arXiv:1202. 1881 (2012).
- Golbeck, Jennifer. "Combining provenance with trust in social networks for semantic web content filtering. " Provenance and Annotation of Data. Springer Berlin Heidelberg, 2006. 101-108.
- Basilico, Justin, and Thomas Hofmann. "A joint framework for collaborative and content filtering. " Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2004.
- Massarani, Leonardo C. "Content-indexing search system and method providing search results consistent with content filtering and blocking policies implemented in a blocking engine. " U. S. Patent No. 6,336,117. 1 Jan. 2002.
- Breese, J. S. , Heckerman, D. , and Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43-52.
- Basu, C. , Hirsh, H. , and Cohen, W. (1998). Recommendation as Classification: Using Social and Content-based Information in Recommendation. In Recommender System Workshop '98. pp. 11-15.
- Ungar, L. H. , and Foster, D. P. (1998) Clustering Methods for Collaborative Filtering. In Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence.
- Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques. " Advances in artificial intelligence 2009 (2009): 4.
- David H. Stern, Ralf Herbrich, and Thore Graepel. Proceedings of the 18th international conference on World Wide Web, page 111--120. New York, NY, USA, ACM, (2009)
Index Terms
Computer Science
Information Sciences
Keywords