Abstract
Lots of researches show that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Meanwhile Web Usage Mining plays an important role in recommender systems and web personalization. However, not many studies have been focused on how to combine the two methods for recommender systems. In this paper, we propose a hybrid recommender system based on ontology and Web Usage Mining. The first step of the approach is extracting features from web documents and constructing relevant concepts. Then build ontology for the web site use the concepts and significant terms extracted from documents. According to the semantic similarity of web documents to cluster them into different semantic themes, the different themes imply different preferences. The hybrid approach integrates semantic knowledge into Web Usage Mining and personalization processes. The experimental results show that the combination of the two approaches can improve the precision rate, coverage rate and matching rate effectively.
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References
Adomavicius, G., Tuzhilin, A.: Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17, 634–749 (2005)
Bell, R., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 95–104 (2007)
Bozsak, E., Ehrig, M., Handschuh, S., Hotho, A., Maedche, A., Motik, B., Oberle, D., Schmitz, C., Staab, S., Stojanovic, L., Stojanovic, N., Studer, R., Stumme, G., Sure, Y., Tane, J., Volz, R., Zacharias, V.: KAON - towards a large scale semantic web. In: Bauknecht, K., Tjoa, A.M., Quirchmayr, G. (eds.) EC-Web 2002. LNCS, vol. 2455, pp. 304–313. Springer, Heidelberg (2002)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI 1998, pp. 43–52 (1998)
Cooley, R.: Web Usage Mining: Discovery and Application of Interesting Patterns from Web Data. PhD thesis, University of Minnesota, Faculty of the Graduate School (2000)
Hotho, A., Staab, S., Stumme, G.: Wordnet improves Text Document Clustering. In: Proc. of the SIGIR 2003 Semantic Web Workshop (2003)
Knappe, R., Bulskov, H., Andreasen, T.: Perspectives on Ontology-based Querying. International Journal of Intelligent Systems (2004)
Liang, W., Zhang, H.Y.: Study of Recommendation Models in E-Commerce Recommendation Systems. Computer Engineering and Applications 42(36), 183–186 (2006)
Lin, D.: Principle-Based Parsing Without Overgeneration. In: Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics (ACL 1993), Columbus, Ohio, pp. 112–120 (1993)
Mao, W., Chu, W.W.: Free text medical document retrieval via phrased-based vector space model. In: Proc. of AMIA 2002, San Antonio, TX (2002)
Mobasher, B., Dai, H.: Improving the effectiveness of collaborative filtering on anonymous web usage data. In: Proc of the IJCAI2001 Workshop on Intelligent Techniques for Web personalization, pp. 49–56. Springer, Berlin (2001)
Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Discovery of aggregate usage profiles for Web personalization. In: Proc. of the WebKDD 2000 Workshop at the ACM SIGKDD 2000, Boston, pp. 142–151 (2000)
Montaner, M., Lopez, B., De La Rosa, J.L.: A taxonomy of recommender agents on theinternet. Artificial Intelligence Review 19(4), 285–330 (2003)
Nakata, K., Voss, A., Juhnke, M., Kreifelts: Collaborative concept extraction from documents. In: Proceedings of the 2nd Int. Conf. on Practical Aspects of Knowledge management (PAKM 1998), Basel, Switzerland, pp. 29–30 (1998)
Pazzani, M., Billsus, D.: Content-based recommendation systems. The Adaptive Web 5, 325–341 (2007)
Porter, M.F.: An lgorithm for suffix stripping. Program 14(3), 130–137 (1980)
Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Schafer, J.B., Konstan, J.A., Riedl, J.: Recommender systems in e-commerce. In: ACM Conference on Electronic Commerce, pp. 158–166 (1999)
Shani, G., Chickering, M., Meek, C.: Mining recommendations from the web. In: Proceedings of the 2008 ACM conference on Recommender systems, Lausanne, Switzerland, pp. 35–42 (2008)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: discovery and application of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)
Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E.G., Milios, E.E.: Semantic similarity methods in wordNet and their application to information retrieval on the web. In: WIDM 2005, pp. 10–16. ACM Press, New York (2005)
Zhang, X., Jing, L., Hu, H., et al.: A comparative study of ontology based term similarity measures on pubmed document clustering. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 115–126. Springer, Heidelberg (2007)
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Wei, L., Lei, S. (2009). Integrated Recommender Systems Based on Ontology and Usage Mining. In: Liu, J., Wu, J., Yao, Y., Nishida, T. (eds) Active Media Technology. AMT 2009. Lecture Notes in Computer Science, vol 5820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04875-3_16
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DOI: https://doi.org/10.1007/978-3-642-04875-3_16
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