Browse > Article
http://dx.doi.org/10.9708/jksci.2012.17.4.083

A Comprehensive Performance Evaluation in Collaborative Filtering  

Yu, Seok-Jong (Dept. of Computer Science, Sookmyung Women's University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.4, 2012 , pp. 83-90 More about this Journal
Abstract
In e-commerce systems that deal with a large number of items, the function of personalized recommendation is essential. Collaborative filtering that is a successful recommendation algorithm, suffers from the sparsity, cold-start, and scalability restrictions. Additionally, this work raises a new flaw of the algorithm, inconsistent performance of recommendation. This is also not measurable by the current MAE-based evaluation that does not consider the deviation of prediction error, and furthermore is performed independently of precision and recall measurement. To evaluate the collaborative filtering comprehensively, this work proposes an extended evaluation model that includes the current criteria such as MAE, Precision, Recall, deviation, and applies it to cluster-based combined collaborative filtering.
Keywords
Recommender System; Collaborative Filtering; Performance Evaluation; MAE Reduction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 S. Gong, "A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering," Journal of Software, Vol. 5, No. 7, July 2010.
2 J. Herlocker, J. Konstan, L. Terveen, and J. Riedl, "Evaluating Collaborative Filtering Recommender Systems," ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 5-53, January 200.   DOI   ScienceOn
3 T. Kim, S. Park, and S. Yang, "Improving Prediction Quality in Collaborative Filtering based on Clustering," Proc. of 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 704-710, 2008.
4 P. Melville, R. J. Mooney, and R. Nagarajan, "Content-Boosted Collaborative Filtering for Improved Recommendations," Proc. of the Eighteenth National Conference on Artificial Intelligence, Edmonton, Canada, pp. 187-192, July 2002.
5 J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, "An Algorithmic Framework for Performing Collaborative Filtering," Proc. of 22nd Annual International ACM SIGIR Conference, Research and Development in Information Retrieval, 1999.
6 G. Groh and C. Ehmig, "Recommendations in Taste Related Domains: Collaborative filtering vs. Social filtering," Proc. of GROUP'07, pp. 127-136, 2007.
7 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, pp. 116- 142, 2004.   DOI   ScienceOn
8 M. Balabanovic and Y. Shoham, "Fab: Content-Based, Collaborative Recommendation," Communications of the ACM, Vol. 40, No. 3, pp. 66-72, 1997.   DOI   ScienceOn
9 Y. Yang and J. Li, "Interest-based Recommendation in Digital Library," Journal of Computer Science, Vol. 1, No. 1, pp. 40-46, 2005.   DOI
10 J. L. Herlocker, J. A. Konstan, and J. T. Riedl, "An Empirical Analysis of Design Choices in Neighborhood-based Collaborative Filtering Systems," Information Retrieval, Vol. 5, pp. 287-310, 2002.   DOI   ScienceOn
11 S. Brin, "Near Neighbor Search in Large Metric Spaces," Proc. of the 21th International Conference on Very Large DataBases, pp. 574-584, 1995.
12 F. Zhang, H. Liu, and J. Chao, "A Two-stage Recommendation Algorithm Based on K-means Clustering In Mobile E-commerce," Journal of Computational Information Systems, Vol. 6, No. 10, pp. 3327-3334, 2010.
13 J. Konstan, D. B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl, "GroupLens: Applying collaborative filtering to Usenet news," Communications of ACM, Vol. 40, No. 3, pp. 77-87, 1997.   DOI   ScienceOn
14 M. Pazzani, "A Framework for Collaborative, Content-Based, and Demographic Filtering," Artificial Intelligence Review, pp. 393-408, Dec 1999.
15 H. Liu and P. Maes, "InterestMap: Harvesting Social Network Profiles for Recommendations," Proc. of the Beyond Personalization Workshop, San Diego, California, pp. 54-49, 2005.
16 B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithm," Proc. of the 10th international conference on World Wide Web, pp. 285-295, 2001.
17 A. Ferman, J. Errico, P. Beek, and M. Sezan, "Content-based Filtering and Personalization Using Structural Metadata," Proc. of the 2nd ACM/IEEE-CS Joint Conference on Digital libraries, NY, USA, pp. 393-393, 2002.
18 H. Kwak,C. Lee,H. Park, and S. Moon, "What is Twitter, a Social Network or a News Media?," Proc. the 19th International World Wide Web Conference, April 26-30, Raleigh NC USA, pp. 591-600, 2010.
19 M. O. Connor and J. Herlocker, "Clustering Items for Collaborative Filtering," Proc. of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, 1999.
20 C. Ding and X. He, "K-Means Clustering via Principal Component Analysis," Proc. of the 21th Int. Conf. on Machine Learning, pp. 225-232, 2004.