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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 739460, 9 pages
http://dx.doi.org/10.1155/2013/739460
Research Article

Personal Recommendation Using a Novel Collaborative Filtering Algorithm in Customer Relationship Management

1College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China
2Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China

Received 2 May 2013; Revised 21 June 2013; Accepted 6 July 2013

Academic Editor: Tinggui Chen

Copyright © 2013 Chonghuan Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Singh, B. P. S. Murthi, and E. Steffes, “Developing a measure of risk adjusted revenue (RAR) in credit cards market: implications for customer relationship management,” European Journal of Operational Research-, vol. 224, no. 2, pp. 425–434, 2013. View at Publisher · View at Google Scholar
  2. B. Öztayşi, T. Kaya, and C. Kahraman, “Performance comparison based on customer relationship management using analytic network process,” Expert Systems with Applications, vol. 38, no. 8, pp. 9788–9798, 2011. View at Publisher · View at Google Scholar
  3. G. Adomavicius 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, pp. 734–749, 2005. View at Publisher · View at Google Scholar
  4. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, no. 1, pp. 5–53, 2004.
  5. Z. Huang, 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, pp. 116–142, 2004. View at Publisher · View at Google Scholar
  6. M. Balabanovi and Y. Shoham, “Fab: content-Based, collaborative recommendation,” Communications of the ACM, vol. 40, no. 3, pp. 66–72, 1997. View at Publisher · View at Google Scholar
  7. M. J. Pazzani, “A framework for collaborative, content-based, and demographic filtering,” Artificial Intelligence Review, vol. 13, no. 5-6, pp. 393–408, 1999. View at Publisher · View at Google Scholar
  8. Y. M. Yang, “An evaluation of statistical approaches to text categorization,” Information Retrieval, vol. 1, no. 1-2, pp. 69–90, 1999. View at Publisher · View at Google Scholar
  9. M. L. Zhang and Z. H. Zhou, “ML-kNN: a lazy learning approach to multi-label learning,” Pattern Recognition, vol. 40, no. 7, pp. 2038–2048, 2007. View at Publisher · View at Google Scholar
  10. J.-Y. Jiang, S.-C. Tsai, and S.-J. Lee, “FSKNN: multi-label text categorization based on fuzzy similarity and k nearest neighbors,” Expert Systems With Applications, vol. 39, no. 3, pp. 2813–2821, 2012. View at Publisher · View at Google Scholar
  11. G. Adomavicius and Z. Jingjing, “Stability of Recommendation Algorithms,” ACM Transactions on Internet Technology, vol. 10, no. 4, 2012. View at Publisher · View at Google Scholar
  12. T. Zhou, L.-L. Jiang, R.-Q. Su, and Y.-C. Zhang, “Effect of initial configuration on network-based recommendation,” Europhysics Letters, vol. 81, no. 5, Article ID 58004, 2008. View at Publisher · View at Google Scholar
  13. Y.-C. Zhang, M. Medo, J. Ren, T. Zhou, T. Li, and F. Yang, “Recommendation model based on opinion diffusion,” Europhysics Letters, vol. 80, no. 6, Article ID 68003, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  14. R. Liu, J.-G. Liu, C.-X. Jia, D. Sun, and B.-H. Wang, “Personal recommendation via unequal resource allocation on bipartite networks,” Physica A, vol. 389, no. 16, pp. 3282–3289, 2010. View at Publisher · View at Google Scholar
  15. S. Maslov and Y.-C. Zhang, “Extracting hidden information from knowledge networks,” Physical Review Letters, vol. 87, no. 24, Article ID 248701, 2001. View at Publisher · View at Google Scholar
  16. J. Ren, T. Zhou, and Y. -C. Zhang, “Information filtering via self-consistent refinement,” Europhysics Letters, vol. 82, no. 5, Article ID 58007, 2008. View at Publisher · View at Google Scholar
  17. R. Liu, C.-X. Jia, T. Zhou, D. Sun, and B.-H. Wang, “Personal recommendation via modified collaborative filtering,” Physica A, vol. 388, no. 4, pp. 462–468, 2009. View at Publisher · View at Google Scholar
  18. J.-M. Yang, K. F. Li, and D.-F. Zhang, “Recommendation based on rational inferences in collaborative filtering,” Knowledge-Based Systems, vol. 22, pp. 105–114, 2009. View at Publisher · View at Google Scholar
  19. Y. Zhao, X. Feng, J. Li, and B. Liu, “Shared collaborative filtering,” in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys '11), pp. 29–36, ACM, 2011. View at Publisher · View at Google Scholar
  20. J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “Generalization of recommender systems: collaborative filtering extended to groups of users and restricted to groups of items,” Expert Systems with Applications, vol. 39, no. 1, pp. 172–186, 2012. View at Publisher · View at Google Scholar
  21. D.-R. Liu, C.-H. Lai, and H. Chiu, “Sequence-based trust in collaborative filtering for document recommendation,” International Journal of Human-Computer Studies, vol. 69, no. 9, pp. 587–601, 2011. View at Publisher · View at Google Scholar
  22. H.-N. Kim, I. Ha, K.-S. Lee, G.-S. Jo, and A. El-Saddik, “Collaborative user modeling for enhanced content filtering in recommender systems,” Decision Support Systems, vol. 51, no. 4, pp. 772–781, 2011. View at Publisher · View at Google Scholar
  23. M. López-Nores, Y. Blanco-Fernández, J. J. Pazos-Arias, and A. Gil-Solla, “Property-based collaborative filtering for health-aware recommender systems,” Expert Systems with Applications, vol. 39, no. 8, pp. 7451–7457, 2012. View at Publisher · View at Google Scholar
  24. C.-F. Tsai and C. Hung, “Cluster ensembles in collaborative filtering recommendation,” Applied Soft Computing, vol. 12, no. 4, pp. 1417–1425, 2012. View at Publisher · View at Google Scholar
  25. K. Choi, D. Yoo, G. Kim, and Y. Suh, “A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis,” Electronic Commerce Research and Applications, vol. 11, no. 4, pp. 309–317. View at Publisher · View at Google Scholar
  26. T. H. Dao, S. R. Jeong, and H. Ahn, “A novel recommendation model of location-based advertising: context-Aware Collaborative Filtering using GA approach,” Expert Systems with Applications, vol. 39, no. 3, pp. 3731–3739, 2012. View at Publisher · View at Google Scholar
  27. A. Eckhardt, “Similarity of users’ (content-based) preference models for Collaborative filtering in few ratings scenario,” Expert Systems with Applications, vol. 39, no. 14, pp. 11511–11516, 2012. View at Publisher · View at Google Scholar
  28. V. Kant and K. K. Bharadwaj, “Enhancing recommendation quality of content-based filtering through collaborative predictions and fuzzy similarity measures,” Procedia Engineering, vol. 38, pp. 939–944, 2012. View at Publisher · View at Google Scholar
  29. C.-H. Lai, D.-R. Liu, and C.-S. Lin, “Novel personal and group-based trust models in collaborative filtering for document recommendation,” Information Sciences, vol. 239, no. 1, pp. 31–49, 2013. View at Publisher · View at Google Scholar
  30. K. Choi and Y. Suh, “A new similarity function for selecting neighbors for each target item in collaborative filtering,” Knowledge-Based Systems, vol. 37, pp. 146–153, 2013. View at Publisher · View at Google Scholar
  31. C.-S. Hwang and Y.-P. Chen, “Using trust in collaborative filtering recommendation,” in New Trends in Applied Artificial Intelligence, pp. 1052–1060, Springer, Berlin, Germany, 2007.
  32. MovieLens dataset, http://www.grouplens.org.
  33. Book-Crossing dataset, http://www.informatik.uni-freiburg.de/~cziegler/BX/.
  34. T. Zhou, J. Ren, M. Medo, and Y.-C. Zhang, “Bipartite network projection and personal recommendation,” Physical Review E, vol. 76, no. 4, Article ID 046115, 2007.