Browse > Article
http://dx.doi.org/10.13088/jiis.2011.17.3.099

Applying Centrality Analysis to Solve the Cold-Start and Sparsity Problems in Collaborative Filtering  

Cho, Yoon-Ho (College of Business Administration, Kookmin University)
Bang, Joung-Hae (College of Business Administration, Kookmin University)
Publication Information
Journal of Intelligence and Information Systems / v.17, no.3, 2011 , pp. 99-114 More about this Journal
Abstract
Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.
Keywords
중심성분석;신규고객추천;희박성;사회연결망;협업필터링;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Scott, J., Social Network Analysis：A Handbook. Thousand Oaks, CA：Sage, 2000.
2 Wasserman, S. and K. Faust, Social network analysis： methods and applications, Cambridge University Press, 1994.
3 Weare, C., W. E. Loges, and N. Oztas, "Email Effects on the Structure of Local Associations： A Social Network Analysis", Social Science Quarterly, Vol.88, No.1(2007), 222-243.   DOI   ScienceOn
4 Park, J. H., Y. H. Cho, and J. K. Kim, "Social Network：A Novel Approach to New Customer Recommendations", Asia Pacific Journal of Intelligent Technology and Management, Vol.15, No.1(2009), 123-140.
5 Park, S. T., Pennock, O. Madani, N. Good, and D. DeCoste, "Naive Filterbots for Robust Cold-Start Recommendations", KDD, 2006.
6 Schein, A. I., A. Popescul, D. M. Pennock, and L. H. Ungar, "Methods and Metrics for Cold-Start Recommendations", SIGIR, 2002.
7 Ryu, Y. U., H. K. Kim, Y. H. Cho, and J. K. Kim, "Peer-oriented content recommendation in a social network", Proceedings of the Sixteenth Workshop on Information Technologies and Systems, (2006), 115-120.
8 Sarwar, B., G. Karypis, J. A. Konstan, and J. Riedl, "Analysis of recommendation algorithms for e-commerce", Proceedings of ACM E-commerce 2000 conference, (2000), 158-167.
9 Sarwar, B., G. Karypis, J. A. Konstan, and J. Riedl, "Application of Dimensionality Reduction in Recommender Systems-A Case Study", Proceedings of ACM WebKDD Workshop, 2000.
10 Kim, J. K. and Y. H. Cho, "Using Web Usage Mining and SVD to Improve E-commerce Recommendation Quality", LNAI, Vol.2891 (2003), 86-97.
11 Melville, P., R. J. Mooney, and R. Nagarajan, "Content-boosted Collaborative Filtering", Proceeding SIGIR 2001 Workshop on Recommender Systems, 2001.
12 Krulwich, B., "Lifestyle Finder：Intelligent User Profiling Using Large-Scale Demographic Data", Artificial Intelligence Magazine, Vol. 18, No.2(1997), 37-45.
13 Kukkonen, H. O., K. Lyytinen, and Y. J. Yoo, "Social Networks and Information System s：Ongoing and Future Research Streams", Journal of the Association for Information Systems, Vol.11(2010), 61-68.
14 Lee, S. K., Y. H. Cho, and S. H. Kim, "Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations", Information Sciences, Vol.180, No.11 (2010), 2142-2155.   DOI   ScienceOn
15 Huang, Z., H. Chen, and D. Zeng, "Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering", ACM Transactions on Information Systems, Vol.22, No.1(2004), 116-142.   DOI   ScienceOn
16 Human, S. E. and K. G. Provan, "Legitimacy Building in the Evolution of Small-Firm Multilateral Networks：A Comparative Study of Success and Demise", Administrative Science Quarterly, Vol.45, No.2(2000), 327-365.   DOI   ScienceOn
17 Frank, O., "Using centrality modeling in network surveys", Social Networks, Vol.24(2002), 385-394.   DOI   ScienceOn
18 Kauffiman, S., The Origins of Order, Oxford University Press, 1993.
19 Kim, H. K., Y. U. Ryu, Y. H. Cho, and J. K. Kim, "Customer-Driven Content Recommendation over a Network of Customers", IEEE Transactions on Systems, Man, and Cybernetics-Part A：Systems and Humans, Forthcoming, 2011.
20 Costenbader, E. and T. W. Valente, "The stability of centrality measures when networks are sampled", Social Networks, Vol.25(2003), 283-307.   DOI   ScienceOn
21 Freeman, L., "Centrality in Social Networks： Conceptual clarification", Social Networks, Vol.1(1979), 215-239.
22 Cho, Y. H. and J. H. Bang, "Social Network Analysis for New Product Recommendation", Asia Pacific Journal of Intelligent Technology and Management, Vol.15, No.4(2009), 123-140.
23 Cho, Y. H. and J. K. Kim, "Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce", Expert Systems with Applications, Vol.26, No.3(2004), 234-246.
24 Choi, S. S., S. H. Cha, and C. Tappert, "A Survey of Binary Similarity and Distance Measures", Journal on Systemics, Cybernetics and Informatics, Vol.8, No.1(2010), 43-48.
25 Bonacich, P., "Power and Centrality：A Family of Measures", American Journal of Sociology, Vol. 92(1987), 1170-1182.   DOI   ScienceOn
26 Borgatti, S. P., "Centrality and network flow", Social Networks, Vol.27, No.1(2005), 55-71.   DOI   ScienceOn
27 Borgatti, S. P. and M. G. Everett, "A Graph-theoretic perspective on centrality", Social Networks, Vol.28(2006), 466-484.   DOI   ScienceOn
28 Cho, Y. H. and I. H. Kim, "Predicting the Performance of Recommender Systems through Social Network Analysis and Artificial Neural Network", Asia Pacific Journal of Intelligent Technology and Management, Vol.16, No.4 (2010), 159-172.
29 Aggarwal, C. C., Z. Sun, and P. S. Yu, "Online Algorithms for Finding Profile Association Rules", Proceedings of the seventh international conference on Information and Knowledge Management, (1998), 86-95.
30 Adomavicius, G. and A. Tuzhilin, "Toward the next generation of recommender systems： A survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6 (2005), 734-749.   DOI
31 Aggarwal, C. C. and P. S. Yu, "Data mining techniques for personalization", Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, Vol.23(2000), 4-9.
32 Zemljic, B. and V. Hlebec, "Reliability of measures of centrality and prominence", Social Networks, Vol.27(2005), 73-88.   DOI   ScienceOn
33 Billsus, D. and M. J. Pazzani, "Learning collaborative information filters", Proceedings of the 15th International Conference on Machine Learning, (1998), 46-54.
34 Yu, K., A. Schwaighofer, V. Tresp, X. Xu, and H. Kriegel, "Probabilistic Memory-based Collaborative Filtering", IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No.1(2004), 56-69.   DOI   ScienceOn