Browse > Article

Effective Recommendation Algorithms for Higher Quality Prediction in Collaborative Filtering  

Kim, Taek-Hun (고려대학교 컴퓨터학과)
Park, Seok-In (연세대학교 컴퓨터과학과)
Yang, Sung-Bong (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.11, 2010 , pp. 1116-1120 More about this Journal
Abstract
In this paper we present two refined neighbor selection algorithms for recommender systems and also show how the attributes of the items can be used for higher prediction quality. The refined neighbor selection algorithms adopt the transitivity-based neighbor selection method using virtual neighbors and alternate neighbors, respectively. The experimental results show that the recommender systems with the proposed algorithms outperform other systems and they can overcome the large scale dataset problem as well as the first rater problem without deteriorating prediction quality.
Keywords
Recommender systems; Collaborative filtering; Neighbor selection algorithm; Prediction quality;
Citations & Related Records
연도 인용수 순위
  • Reference
1 X. N. Lam, T. Vu, T.D. Le, and A.D.Duong, "Addressing Cold-start Problem in Recommendation Systems," Proc. of ICUIMC'08, 2008.
2 MovieLens dataset, GroupLens Research Group, url: http://www.grouplens.org/.
3 J. S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. of UAI 1998 Conference, 1998.
4 H. Nguyen and P. Haddawy, "The Decision- Theoretic Interactive Video Advisor," Proc. of UAI 1999 Conference, 1999.
5 R. J. Mooney and L. Roy, "Content-Based Book Recommending Using Learning for Text Categorization," Proc. of ACM Conference on Digital Libraries, 2000.
6 T. H. Kim and S. B. Yang, "Using Attributes to Improve Prediction Quality in Collaborative Filtering," Proc. of EC-Web 2004, 2004.
7 B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl, "Analysis of Recommendation Algorithms for E-Commerce," Proc. of ACM E-Commerce 2000, 2000.
8 B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl, "Recommender Systems for Large-Scale E-Commerce: Scalable Neighborhood Formation Using Clustering," Proc. of the Fifth International Conference on Computer and Information Technology, 2002.
9 J. A. Konstan, B. Miller, D. Maltz, J. L. Herlocker, L. Gordon, and J. T. Riedl, "GroupLens: Applying Collaborative Filtering to Usenet News," Communications of the ACM, vol.40, 1997.
10 J. Riedl, T. Beaupre, J. Sanders, "Research Challenges in Recommenders," Proc. of ACM Recommender Systems 2009 Tutorial, 2009.