Ranking Based Approach to Maximize Utility of Recommender Systems
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - November Issue 2013 by IJCTT Journal|
|Volume-5 Issue-1 |
|Year of Publication : 2013|
|Authors :S.Ganesh Kumar , P.Hari Krishna.|
S.Ganesh Kumar , P.Hari Krishna."Ranking Based Approach to Maximize Utility of Recommender Systems"International Journal of Computer Trends and Technology (IJCTT),V5(1):26-31 November Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- E-commerce applications that sell products online need to recommend suitable products to customers to fasten their decision making. The recommender systems are required in order to help users and also the businesses alike. There were many algorithms that came into existence to built recommender systems. However they focused on recommendation accuracy. They did not concentrate much on recommendation quality like diversity of recommendations. This paper introduces many item ranking algorithms that can produce diverse recommendations. While generating recommendations transactions of all users are considered. A prototype application is built to test the efficiency of the proposed recommender system. The empirical results revealed that the proposed ranking-based techniques for diverse recommendations are effective and can be used in real world applications.
 W. Knight, “Info-Mania’ Dents IQ More than Marijuana,” New Scientist.comNews, http://www.newscientist.com/article.ns?id= dn7298, 2005.
 G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
 D. Billsus and M. Pazzani, “Learning Collaborative Information Filters,” Proc. Int’l Conf. Machine Learning, 1998.
 Y. Koren, “Collaborative Filtering with Temporal Dynamics,” Proc. 15th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 447-456, 2009.
 P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. Computer Supported Cooperative Work Conf., 1994.
 B.M. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of Recommender Algorithms for E-Commerce,” Proc. ACM Conf. Electronic Commerce, pp. 158-167, 2000.
 J.L. Herlocker, J.A. Konstan, L.G. Terveen, and J. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
 S.M. McNee, J. Riedl, and J.A. Konstan, “Being Accurate Is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems,” Proc. Conf. Human Factors in Computing Systems, pp. 1097-1101, 2006.
 K. Bradley and B. Smyth, “Improving Recommendation Diversity,” Proc. 12th Irish Conf. Artificial Intelligence and Cognitive Science, 2001.
 E. Brynjolfsson, Y.J. Hu, and D. Simester, “Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales,” Management Science, vol. 57, no. 8, pp. 1373-1386, 2011.
 D. Fleder and K. Hosanagar, “Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity,” Management Science, vol. 55, no. 5, pp. 697-712, 2009.
 B. Smyth and P. McClave, “Similarity vs. Diversity,” Proc. Fourth Int’l Conf. Case-Based Reasoning: Case-Based Reasoning Research and Development, 2001.
 M. Zhang and N. Hurley, “Avoiding Monotony: Improving the Diversity of Recommendation Lists,” Proc. ACM Conf. Recommender Systems, pp. 123-130, 2008.
 C-N. Ziegler, S.M. McNee, J.A. Konstan, and G. Lausen, “Improving Recommendation Lists through Topic Diversification,” Proc. 14th Int’l World Wide Web Conf., pp. 22- 32, 2005.
 E. Brynjolfsson, Y. Hu, and M.D. Smith, “Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers,” Management Science, vol. 49, no. 11, pp. 1580-1596, 2003.
Keywords :— Recommendations, recommender system, ranking techniques, recommendation diversity, collaborative filtering