Abstract
Each user accesses a Website with certain interests. The interest can be manifested by the sequence of each Web user access. The access paths of all Web users can be clustered. The effectiveness and efficiency are two problems in clustering algorithms. This paper provides a clustering algorithm for personalized Web recommendation. It is path clustering based on competitive agglomeration (PCCA). The path similarity and the center of a cluster are defined for the proposed algorithm. The algorithm relies on competitive agglomeration to get best cluster numbers automatically. Recommending based on the algorithm doesn’t disturb users and needn’t any registration information. Experiments are performed to compare the proposed algorithm with two other algorithms and the results show that the improvement of recommending performance is significant.
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References
Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: 9th International Conference on Tools with Artificial Intelligence, ICTAI97, Newport Beach, CA, USA, pp. 558–567. IEEE Computer Society, Los Alamitos (1997)
Cooley, R.: The use of Web structure and content to identify subjectively interesting Web usage patterns. ACM Transactions on Internet Technology (TOIT) 3(2), 93–116 (2003)
Nasraoui, O., Frigui, H., Krishnapuram, R.: Extracting Web user profiles using relational competitive fuzzy clustering. International Journal on Artificial Intelligence Tools 9(4), 509–526 (2000)
Mobasher, B., Dai, H., Luo, T.: Discovery and evaluation of aggregate usage profiles for Web personalization. Data Mining and Knowledge Discovery 6(1), 61–82 (2002)
Enembreck, F., Barthès, J.-P.A.: Agents for collaborative filtering. In: Klusch, M., Omicini, A., Ossowski, S., Laamanen, H. (eds.) CIA 2003. LNCS, vol. 2782, pp. 184–191. Springer, Heidelberg (2003)
Briggs, P., Smyth, B.: On the Use of Collaborative Filtering Techniques for the Prediction of Web Search Result Rank. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 380–383. Springer, Heidelberg (2004)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. Communications of the ACM 43(8), 142–151 (2000)
Huang, Z.: Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery 2(3), 283–304 (1998)
Wang, S., Gao, W., Li, J.T.: Path clustering: discovering the knowledge in the Website. Journal of Computer Research & Development 38(4), 482–486 (2001)
Frigui, H., Krishnapuram, R.: A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers. In: Conference on Computer Vision and Pattern Recognition (CVPR 1996), pp. 550–555. IEEE Computer Society, San Francisco (1996)
Chun, J., Oh, J., Kwon, S.: Simulating the effectiveness of using association rules for recommendation systems. In: AsiaSim 2004, pp. 306–314. Springer, Jeju Island (2005)
Ji, G.L., Sun, Z.H.: An algorithm for mining optimized confidence quantitative association rules. Journal of Southeast University (Natural Science Edition) 31(2), 31–34 (2001)
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© 2006 Springer-Verlag Berlin Heidelberg
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Yu, Y., Lin, H., Yu, Y., Chen, C. (2006). Personalized Web Recommendation Based on Path Clustering. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_31
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DOI: https://doi.org/10.1007/11766254_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34638-8
Online ISBN: 978-3-540-34639-5
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