Web Page Recommendation Based on Semantic Web Usage Mining

  • Soheila Abrishami
  • Mahmoud Naghibzadeh
  • Mehrdad Jalali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)


The growth of the web has created a big challenge for directing the user to the Web pages in their areas of interest. Meanwhile, web usage mining plays an important role in finding these areas of interest based on user’s previous actions. The extracted patterns in web usage mining are useful in various applications such as recommendation. Classical web usage mining does not take semantic knowledge and content into pattern generations. Recent researches show that ontology, as background knowledge, can improve pattern’s quality. This work aims to design a hybrid recommendation system based on integrating semantic information with Web usage mining and page clustering based on semantic similarity. Since the Web pages are seen as ontology individuals, frequent navigational patterns are in the form of ontology instances instead of Web page addresses, and page clustering is done using semantic similarity. The result is used for generating web page recommendations to users. The recommender engine presented in this paper which is based on semantic patterns and page clustering, creates a list of appropriate recommendations. The results of the implementation of this hybrid recommendation system indicate that integrating semantic information and page access sequence into the patterns yields more accurate recommendations.


Web usage mining semantic web ontology web page recommendation page clustering 


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  1. 1.
    Berendt, B., Hotho, A., Stumme, G.: Towards Semantic Web Mining. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 264–278. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge and information systems 1, 5–32 (1999)Google Scholar
  3. 3.
    Wei, L., Lei, S.: Integrated Recommender Systems Based on Ontology and Usage Mining. Active Media Technology, 114–125 (2009)Google Scholar
  4. 4.
    Samizadeh, R., Ghelichkhani, B.: Use of semantic similarity and web usage mining to alleviate the drawbacks of user-based collaborative filtering recommender systems use. International Journal of Industrial Engineering and Production Research (IJIE), English (2010)Google Scholar
  5. 5.
    Etminani, K., Delui, A.R., Naghibzadeh, M.: Overlapped ontology partitioning based on semantic similarity measures. In: 2010 5th International Symposium on Telecommunications (IST), pp. 1013–1018. IEEE (2010)Google Scholar
  6. 6.
    Zaki, M.J.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42, 31–60 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge and Data Engineering 16, 1424–1440 (2004)CrossRefGoogle Scholar
  8. 8.
    Maedche, A., Zacharias, V.: Clustering Ontology-Based Metadata in the Semantic Web. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 383–408. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Dai, H., Mobasher, B.: Integrating semantic knowledge with web usage mining for personalization. WebMining: Applications and Techniques.[20082 06211] (2009),
  10. 10.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Improving the effectiveness of collaborative filtering on anonymous web usage data. In: IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization ITWP01 2001 (2001)Google Scholar
  11. 11.
  12. 12.
  13. 13.
    Mabroukeh, N.R., Ezeife, C.I.: Using domain ontology for semantic web usage mining and next page prediction. In: Information and Knowledge Management, pp. 1677–1680. ACM (2009)Google Scholar
  14. 14.
    Adda, M., Valtchev, P., Missaoui, R., Djeraba, C.: Toward recommendation based on ontology-powered web-usage mining. IEEE Internet Computing 45–52 (2007)Google Scholar
  15. 15.
    Nakagawa, M., Mobasher, B.: Impact of site characteristics on recommendation models based on association rules and sequential patterns. In: IJCAI 2003 Workshop on Intelligent Techniques for Web Personalization (2003)Google Scholar
  16. 16.
    Stumme, G., Hotho, A., Berendt, B.: Usage Mining for and on the Semantic Web: next generation data mining. In: NSF Workshop (2002)Google Scholar
  17. 17.
    Stumme, G., Hotho, A., Berendt, B.: Semantic web mining: State of the art and future directions. Web Semantics: Science, Services and Agents on the World Wide Web 4, 124–143 (2006)CrossRefGoogle Scholar
  18. 18.
    Senkul, P., Salin, S.: Improving pattern quality in web usage mining by using semantic information. Knowledge and information systems 30, 527–541 (2012)CrossRefGoogle Scholar
  19. 19.
    Yilmaz, H., Senkul, P.: Using Ontology and Sequence Information for Extracting Behavior Patterns from Web Navigation Logs. In: 2010 IEEE International Conference on Data Mining Workshops, pp. 549-556. IEEE (2010) Google Scholar
  20. 20.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: 5-th Berkeley Symposium on Mathematical Statistics and Probability, California, USA, p. 14 (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Soheila Abrishami
    • 1
  • Mahmoud Naghibzadeh
    • 2
  • Mehrdad Jalali
    • 1
  1. 1.Department of Computer EngineeringAzad University of MashhadMashhadIran
  2. 2.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran

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