This Article 
 Bibliographic References 
 Add to: 
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
September/October 2007 (vol. 22 no. 5)
pp. 68-78
Zan Huang, Pennsylvania State University
Daniel Zeng, University of Arizona
Hsinchun Chen, University of Arizona
An evaluation of six recommendation algorithms on e-commerce-related data sets is an initial step toward a metalevel guideline for choosing the best algorithm for a given application with certain data characteristics. Two of the evaluated algorithms are the popular user-based and item-based correlation/similarity algorithms. The other four algorithms attempt to meet the challenge of data sparsity through dimensionality reduction, generative models, spreading activation, or link analysis. Initial experimental comparisons indicate that the link-analysis algorithm achieves the best overall performance across several e-commerce data sets.

1. P. Resnick et al., "An Open Architecture for Collaborative Filtering of Netnews," ACM Conf. Computer-Supported Cooperative Work, ACM Press, 1994, pp. 175–186.
2. J.S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI 98), Morgan Kaufmann, 1998, pp. 43–52.
3. P. Resnick and H. Varian, "Recommender Systems," Comm. ACM, vol. 40, no. 3, 1997, pp. 56–58.
4. T. Hofmann, "Latent Semantic Models for Collaborative Filtering," ACM Trans. Information Systems, vol. 22, no. 1, 2004, pp. 89–115.
5. Z. Huang, H. Chen, and D. Zeng, "Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering," ACM Trans. Information Systems, vol. 22, no. 1, 2004, pp. 116–142.
6. M. Papagelis, D. Plexousakis, and T. Kutsuras, "Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences," Proc. 3rd Int'l. Conf. Trust Management (iTrust 05), Springer, 2005, pp. 224–239.
7. B. Sarwar et al., "Application of Dimensionality Reduction in Recommender Systems: A Case Study," Proc. WebKDD Workshop at the ACM SIGKDD, 2000; .
8. J.L. Herlocker et al., "Evaluating Collaborative Filtering Recommender Systems," ACM Trans. Information Systems, vol. 22, no. 1, 2004, pp. 5–53.
9. M. Deshpande and G. Karypis, "Item-Based Top-N Recommendation Algorithms," ACM Trans. Information Systems, vol. 22, no. 1, 2004, pp. 143–177.
10. L.H. Ungar and D.P. Foster, "A Formal Statistical Approach to Collaborative Filtering," Proc. 1998 Conf. Automated Learning and Discovery (CONALD98), 1998; .
11. J. Kleinberg, "Authoritative Sources in a Hyperlinked Environment," J. ACM, vol. 46, no. 5, 1999, pp. 604–632.
12. S. Brin and L. Page, "The Anatomy of a Large-Scale Hypertextual Web Search Engine," Proc. 7th Int'l World Wide Web Conf., Elsevier, 1998, pp. 107–117.
13. Z. Huang, D. Zeng, and H. Chen, "A Link Analysis Approach to Recommendation under Sparse Data," Proc. 2004 Americas Conf. Information Systems, 2004; huz2/Zan/paperslink.recommend.pdf.

Index Terms:
recommender systems, collaborative filtering, algorithm design and evaluation, e-commerce
Zan Huang, Daniel Zeng, Hsinchun Chen, "A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce," IEEE Intelligent Systems, vol. 22, no. 5, pp. 68-78, Sept.-Oct. 2007, doi:10.1109/MIS.2007.80
Usage of this product signifies your acceptance of the Terms of Use.