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Survey on Recommendation System

by Lipi Shah, Hetal Gaudani, Prem Balani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 7
Year of Publication: 2016
Authors: Lipi Shah, Hetal Gaudani, Prem Balani
10.5120/ijca2016908821

Lipi Shah, Hetal Gaudani, Prem Balani . Survey on Recommendation System. International Journal of Computer Applications. 137, 7 ( March 2016), 43-49. DOI=10.5120/ijca2016908821

@article{ 10.5120/ijca2016908821,
author = { Lipi Shah, Hetal Gaudani, Prem Balani },
title = { Survey on Recommendation System },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 7 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 43-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number7/24291-2016908821/ },
doi = { 10.5120/ijca2016908821 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:46.899941+05:30
%A Lipi Shah
%A Hetal Gaudani
%A Prem Balani
%T Survey on Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 7
%P 43-49
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes the overview of recommendation system. The recommendation system is the sub-part of the data mining field. This is the era of the e-commerce business. Recommender systems are used to assists the enterprise to implement one-to-one marketing strategies. These type of strategies offer several advantages like establishing the customer loyalty, increase the probability of cross-selling, fulfilling the customer need by presenting the items or products of customer interest. The recommendation system (RS) is crucial in many applications on the web. The recommendation system is mainly classified into following three categories: content-based, collaborative-based and hybrid approaches. Different categories have its own advantages as well as disadvantages .This paper describes the different techniques in each category and the issues in each category.

References
  1. S. Robertson and S. Walker, “Threshold Setting in Adaptive Filtering,” J. Documentation, vol. 56, pp. 312-331, 2000.
  2. M. Pazzani and D. Billsus, “Learning and Revising User Profiles The Identification of Interesting Web Sites,” Machine Learning, vol. 27, pp. 313-331, 1997.
  3. J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 230–237, Berkeley, CA, 1999. ACM Press.
  4. W. Hill, L. Stead, M. Rosenstein, and G. Furnas, “Recommending and Evaluating Choices in a Virtual Community of Use,” Proc. Conf. Human Factors in Computing Systems, 1995.
  5. Burke R. Hybrid recommender systems: survey and experiments. User Model User-adapted Interact 2002;(12)
  6. 331–70
  7. G. Somlo and A. Howe, “Adaptive Lightweight Text Filtering,” Proc. Fourth Int’l Symp. Intelligent Data Analysis, 2001.
  8. D. Pavlov and D. Pennock, “A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains,” Proc. 16th Ann. Conf. Neural Information Processing Systems (NIPS ’02), 2002.
  9. M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,” Comm. ACM, vol. 40, no. 3, pp. 66-72, 1997.
  10. J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon, and J. Riedl, “GroupLens: Applying Collaborative Filtering toUsenet News,” Comm. ACM, vol. 40, no. 3, pp. 77-87, 1997.
  11. M. Pazzani and D. Billsus, “Learning and Revising User Profiles:The Identification of Interesting Web Sites,” Machine Learning,vol. 27, pp. 313-331, 1997.
  12. L. Getoor and M. Sahami, “Using Probabilistic Relational Models for Collaborative Filtering,” Proc. Workshop Web Usage Analysis and User Profiling (WEBKDD ’99), Aug. 1999.
  13. B. Sheth and P. Maes, “Evolving Agents for Personalized Information Filtering,” Proc. Ninth IEEE Conf. Artificial Intelligence for Applications, 1993.
  14. Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems, Computer (2009).
  15. Y.-H. Chien and E.I. George, “A Bayesian Model for Collaborative Filtering,” Proc. Seventh Int’l Workshop Artificial Intelligence and Statistics, 1999.
  16. Greg Linden, Brent Smith, and Jeremy,”Amazon.com Recommendations Item-to-Item Collaborative Filtering ”,
  17. IEEE (2003).
  18. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States
  19. Schwab I, Kobsa A, Koychev I. Learning user interests through positive examples using content analysis and collaborative filtering. Draft from Fraunhofer Institute for Applied Information Technology, Germany; 2001.
  20. Iskold, A., Rethinking Recommendation Engines,http://alexiskold.wordpress.com/2008/02/25/rethinking-recommendation-engines/ , 25 February 2008, Rethinking Recommendation Engines.
  21. G. Salton, Automatic Text Processing. Addison-Wesley, 1989.
  22. Pronk, V., Verhaegh, W., Proidl, A., and Tiemann, M., Incorporating user control into recommender systems based on naive bayesian classification. In RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pages 73–80,2007
  23. Witten I. H. and Frank I. Data Mining, Morgan Kaufman Publishers, an Francisco, 2000.
  24. Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M. Combining content- based and collaborative filters in an online newspaper. In: Proceedings of ACM SIGIR workshop on recommender systems: algorithms and evaluation, Berkeley, California; 1999.
  25. Ujjin, S.,Bentley,P.J., Building a Lifestyle Recommender System, Poster Proceedings of the 10th International World, 2001.
  26. Basu C, Hirsh H, Cohen W. Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the 15th national conference on artificial intelligence, Madison, WI; 1998. p. 714–20.
  27. E. Rich, “User Modeling via Stereotypes,” Cognitive Science, vol. 3, no. 4, pp. 329-354, 1979.
  28. Billsus D, Pazzani MJ. A hybrid user model for news story classification. In: Kay J, editor. In: Proceedings of the seventh international conference on user modeling, Banff, Canada. Springer-Verlag, New York; 1999. p. 99–108.
  29. MacManus, R., A guide to Recommender Systems,http://www.readwriteweb.com/archives/recommendation_systems_where_we_need_to_go.php , 26 january 2009 A guide to Recommender Systems
  30. Mooney RJ, Roy L. Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on digital libraries. ACM; 2000. p. 195–204.
  31. Y. Zhang, J. Callan, and T. Minka, “Novelty and Redundancy Detection in Adaptive Filtering,” Proc. 25th Ann. Int’l ACM SIGIR Conf., pp. 81-88, 2002.
  32. John S. Breese, David Heckerman and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertaintyin Artificial Intelligence, pages 43-52, July 1998.
  33. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item- Based Collaborative Filtering Recommendation Algorithm,” Proc. 10th Int’l WWW conf, 2001.
  34. Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wang, “Friendbook: A Semantic-based Friend Recommendation System for Social Networks” IEEE TRANSACTIONS ON MOBILE COMPUTING,VOL. 13, NO. 99, MAY2014.
  35. Wintergreen, T., The Music Genome Project®, http://www.pandora.com/mgp.shtml, 2000
  36. John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July 1998.
  37. J.S. Armstrong, Principles of Forecasting—A Handbook for Researchers and Partitioners. Kluwer Academic, 2001.
  38. Y. Zhang and J. Callan, “Maximum Likelihood Estimation for Filtering Thresholds,” Proc. 24th Ann. Int’l ACM SIGIR Conf., 2001.
  39. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, “Eigentaste: A Constant Time Collaborative Filtering Algorithm,” Information Retrieval J., vol. 4, no. 2, pp. 133-151, July 2001.
  40. Smyth B, Cotter P. A personalized TV listings service for the digital TV age. J Knowl-Based Syst 2000;13(23):53 9.
  41. M.J.D. Powell, Approximation Theory and Methods. Cambridge Univ. Press, 1981.
  42. P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. 1994 Computer Supported Cooperative Work Conf., 1994.
  43. E. Rich, “User Modeling via Stereotypes,” Cognitive Science, vol. 3, no. 4, pp. 329-354, 1979.
  44. G. Salton, Automatic Text Processing. Addison-Wesley, 1989.
Index Terms

Computer Science
Information Sciences

Keywords

Recommendation system content based filtering collaborative filtering hybrid approach.

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