Improving an aggregate recommendation diversity Using ranking-based tactics
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © - September Issue 2013 by IJCTT Journal | ||
| Volume-4 Issue-9 | ||
| Year of Publication : 2013 | ||
| Authors :K. Satya Reddy, A. Raghavendra rao |
K. Satya Reddy, A. Raghavendra rao "Improving an aggregate recommendation diversity Using ranking-based tactics"International Journal of Computer Trends and Technology (IJCTT),V4(9):3178-3183 September Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- The importance of Recommender systems is becoming more and more to single users and mluti users by providing personalized recommendations. Many of the algorithms proposed in recommender systems literature have been concentrating on improving the recommendation efficiency rate and other important issues of recommendation quality like diversity of recommendations, etc have been discussed. Trough this paper, we make you aware of various item ranking techniques that will generate recommendations which have considerably higher aggregate diversity over all users while maintaining comparative-levels of recommendation accuracy. Comprehensive empirical evaluation uniformly indicates the diversity in improving the proposed techniques by using several real-world rating datasets and rating prediction algorithms.
References-
[1]. 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.
[2]. B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Analysis of Recommender Algorithms for E-Commerce,” ACM E-Commerce 2000 Conf., pp.158-167, 2000.
[3]. M. Zhang and N. Hurley, “Avoiding monotony: improving the diversity of recommendation lists,” Proc. of the 2008 ACM Conf. on Recommender systems, pp. 123-130, 2008.
[4]. K. Greene, “The $1 million Netflix challenge,” Technology Review.www.technologyreview.com/read_article.aspx?Id=1758 7&ch=biztech, October 6, 2006.
[5]. D. Billsus and M. Pazzani, “Learning Collaborative Information Filters,” Proc. Int’l Conf. Machine Learning, 1998.
[6]. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence, 1998.
[7]. M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation,” Comm. ACM, 40(3), pp. 66- 72, 1997.
[8]. G. Linden, B. Smith, and J. York. Amazon. Com Recommendations: Item-to-Item Collaborative Filtering. 2003.
[9]. Netflix. Netflix prize. http://www.netflixprize.com/.
[10]. L.J. Herlocker, A.J. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work table of contents Philadelphia, Pennsylvania, United States, pages 241–250, 2000.
[11]. Google Video. http://video.google.com/.
[12]. Yahoo! Video. http://video.yahoo.com/.
[13]. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques-2011 Gediminas Adomavicius, Member, IEEE, and YoungOk Kwon
[14]. YouTube. http://www.youtube.com/.
[15]. Collaborative Filtering for the Netflix Prize Hao Zhang Partner: Sahand Negahban This email address is being protected from spambots. You need JavaScript enabled to view it. Department of EECS, University of California, Berkeley
Keywords — : SRAM; Built-In Self-Repair (BISR); Built-In Self Test (BIST); Built-In Address-Analysis (BIAA); Compiler.