CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

by Poonam B. Thorat, R. M. Goudar, Sunita Barve
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 4
Year of Publication: 2015
Authors: Poonam B. Thorat, R. M. Goudar, Sunita Barve
10.5120/19308-0760

Poonam B. Thorat, R. M. Goudar, Sunita Barve . Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. International Journal of Computer Applications. 110, 4 ( January 2015), 31-36. DOI=10.5120/19308-0760

@article{ 10.5120/19308-0760,
author = { Poonam B. Thorat, R. M. Goudar, Sunita Barve },
title = { Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 4 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number4/19308-0760/ },
doi = { 10.5120/19308-0760 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:40.526537+05:30
%A Poonam B. Thorat
%A R. M. Goudar
%A Sunita Barve
%T Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 4
%P 31-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E-commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

References
  1. Adomavicius, G. ; Tuzhilin, A. , "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions," Knowledge and Data Engineering, IEEE Transactions on , vol. 17, no. 6, pp. 734,749, June 2005.
  2. F. Ricci, L. Rokach, B. Shapira, P. B. (Eds. ), "Kantor Recommender Systems Handbook", first ed. , 2011, XXX, 842 p. 20 illus
  3. J. Bobadilla, F. Serradilla, "The effect of sparsity on collaborative filtering metrics", in: Australian Database Conference, 2009, pp. 9–17.
  4. J. Bobadilla, A. Hernando, F. Ortega, J. Bernal, "A framework for collaborative filtering recommender systems", Expert Systems with Applications 38 (12) (2011) 14609–14623.
  5. J. B. Schafer, D. Frankowski, J. Herlocker, S. Sen,"Collaborative filtering recommender systems", in: P. Brusilovsky, A. Kobsa, W. Nejdl (Eds. ), The Adaptive Web, 2007, pp. 291–324 .
  6. B. Sarwar, G. Karypis, J. Konstan, J. Riedl, "Application of dimensionality reduction in recommender system – a case study", in: ACM WebKDD Workshop, 2000b, pp. 264–272.
  7. Y. Koren, R. Bell, CH. Volinsky, "Matrix factorization techniques dor recommender systems", IEEE Computer 42 (8) (2009) 42–49.
  8. X. Luo, Y. Xia, Q. Zhu, "Applying the learning rate adaptation to the matrix factorization based collaborative filtering", Knowledge Based Systems 37 (2013) 154–164.
  9. X. Luo, Y. Xia, Q. Zhu, "Incremental collaborative filtering recommender based on regularized matrix factorization", Knowledge-Based Systems 27 (2012) 271–280.
  10. G. Takacs, I. Pilaszy, B. Nemeth, D. Tikk, "Scalable collaborative filtering approaches for large recommender systems", Journal of Machine Learning Research 10 (2009) 623–656.
  11. M. G. Vozalis, K. G. Margaritis, "Using SVD and demographic data for the enhancement of generalized collaborative filtering", Information Sciences 177 (2007) 3017–3037.
  12. R. Burke, "Hybrid recommender systems: survey and experiments", User Modeling and User-Adapted Interaction 12 (4) (2002) 331–370.
  13. M. Gemmis, P. Lops, G. Semeraro, P. Basile, "Integrating tags in a semantic content-based recommender", in: Proceedings of the 2008 ACM conference on Recommender Systems, 2008, pp. 163–170.
  14. M. Pazzani, "A framework for collaborative, content-based, and demographic filtering", Artificial Intelligence Review-Special Issue on Data Mining on the Internet 13 (5-6) (1999) 393–408.
  15. S. Zhang, W. Wang, J. Ford, F. Makedon, "Using singular value decomposition approximation for collaborative filtering", in: IEEE International Conference on E-Commerce Technology, 2005, pp. 1–8.
  16. M. Balabanovic, Y. Shoham, "Content-based, collaborative recommendation", Communications of the ACM 40 (3) (1997) 66–72.
  17. F. Cacheda, V. Carneiro, D. Fernandez, V. Formoso, "Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender Systems", ACM Transactions on the Web 5 (1) (2011). Article 2.
  18. C. Basu, H. Hirsh, W. Cohen, "Recommendation as classification: using social and content-based information in recommendation", in: Proceedings of the Fifteenth National Conference on Artificial Intelligence, 1998, pp. 714–720.
  19. Lops, Pasquale, Marco De Gemmis, and Giovanni Semeraro. "Content-based recommender systems: State of the art and trends. " Recommender systems handbook. Springer US, 2011. 73-105.
  20. Sutton, R. S. , Barto, A. G. , 1998. "Reinforcement Learning: An Introduction". MIT Press, Cambridge, MA.
  21. K. Heung-Nam, E. S. Abdulmotaleb, J. Geun-Sik, "Collaborative error-reflected models for cold-start recommender systems", Decision Support Systems 51 (3) (2011) 519–531.
  22. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Reidl, "Item-based collaborative filtering recommendation algorithms," in ACM www '01, pp. 285–295, ACM, 2001.
  23. J. Bennett, S. Lanning, "The Netflix prize, in: Proceedings of KDD Cup and Workshop", 2007, pp. 3–6.
  24. R. M. Bell, Y. Koren, "Lessons from the Netflix prize challenge", ACM SIGKDD Explorations Newsletter 9 (2007) 75–79.
  25. A. Narayanan, V. Shmatikov, "Robust de-anonymization of large sparse datasets", in: IEEE Symposium on Security and Privacy, 2008, pp. 111–125.
  26. Linyuan Lua, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang,Tao Zhou, et al. "Recommender systems" Physics Reports 519. 1 (2012): 1-49.
  27. Sánchez Sánchez, José Luis. "Improving Collaborative Filtering Based Recommender Systems Using Pareto Dominance". Diss. E_Informatica, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Content-Based filtering collaborative-filtering Hybrid Recommendation System Data sparsity

Powered by PhDFocusTM