Semantically Enhanced Collaborative Filtering on the Web

  • Bamshad Mobasher
  • Xin Jin
  • Yanzan Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3209)


Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificing recommendation or prediction accuracy. Item-based algorithms avoid the bottleneck in computing user-user correlations by first considering the relationships among items and performing similarity computations in a reduced space. Because the computation of item similarities is independent of the methods used for generating predictions, multiple knowledge sources, including structured semantic information about items, can be brought to bear in determining similarities among items. The integration of semantic similarities for items with rating- or usage-based similarities allows the system to make inferences based on the underlying reasons for which a user may or may not be interested in a particular item. Furthermore, in cases where little or no rating (or usage) information is available (such as in the case of newly added items, or in very sparse data sets), the system can still use the semantic similarities to provide reasonable recommendations for users. In this paper, we introduce an approach for semantically enhanced collaborative filtering in which structured semantic knowledge about items, extracted automatically from the Web based on domain-specific reference ontologies, is used in conjunction with user-item mappings to create a combined similarity measure and generate predictions. Our experimental results demonstrate that the integrated approach yields significant advantages both in terms of improving accuracy, as well as in dealing with very sparse data sets or new items.


Singular Value Decomposition Recommender System Semantic Similarity Latent Semantic Analysis Collaborative Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Aggarwal, C.C., Wolf, J.L., Yu, P.S.: A new method for similarity indexing for market data. In: Proceedings of the 1999 ACM SIGMOD Conference, Philadelphia, PA (June 1999)Google Scholar
  2. 2.
    Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: Proceedings of the the 15th National Conference on Artificial Intelligence (AAAI 1998), Madison, WI (July 1998)Google Scholar
  3. 3.
    Berendt, B., Hotho, A., Stumme, G.: Towards semantic web mining. In: Proceedings of the First International Semantic Web Conference (ISWC 2002), Sardinia, Italy (June 2002)Google Scholar
  4. 4.
    Berry, M.W., Dumais, S.T., Brien, G.W.O.: Using linear algebra for intelligent information retrieval. SIAM Review 37, 573–595 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of the International Conference on Machine Learning, Madison, WI (1998)Google Scholar
  6. 6.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR 1999 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, California (August 1999)Google Scholar
  7. 7.
    Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., Slattery, S.: Learning to construct knowledge bases from the world wide web. Artificial Intelligence 118(1-2), 69–113 (2000)zbMATHCrossRefGoogle Scholar
  8. 8.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems 22(1), 1–34 (2004)CrossRefGoogle Scholar
  9. 9.
    Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Transactions on Information Systems 21(1), 63–94 (2003)CrossRefGoogle Scholar
  10. 10.
    Ghani, R., Fano, A.: Building recommender systems using a knowledge base of product semantics. In: Proceedings of the Workshop on Recommendation and Personalization in E-Commerce, at the 2nd Int’l Conf. on Adaptive Hypermedia and Adaptive Web Based Systems, Malaga, Spain (May 2002)Google Scholar
  11. 11.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval (SIGIR 1999), Berkeley, CA (August 1999)Google Scholar
  12. 12.
    Horrocks, I.: Daml+oil: A reasonable web ontology language. In: Proceedings of the 8th International Conference on Extending Database Technology, Prague, Czech Republic, March 2002, pp. 2–13. Springer, Heidelberg (2002)Google Scholar
  13. 13.
    Hotho, A., Maedche, A., Staab, S.: Ontology-based text clustering. In: Proceedings of the IJCAI 2001 Workshop Text Learning: Beyond Supervision, Seattle, WA (August 2001)Google Scholar
  14. 14.
    Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM 40(3) (1997)Google Scholar
  15. 15.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering. In: Proceedings of the SIGIR 2001 Workshop on Recommender Systems, New Orleans, LA (September 2001)Google Scholar
  16. 16.
    Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Communications of the ACM 43(8), 142–151 (2000)CrossRefGoogle Scholar
  17. 17.
    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd ACM Workshop on Web Information and Data Management (WIDM 2001), Atlanta, Georgia (November 2001)Google Scholar
  18. 18.
    Mobasher, B., Dai, H., Nakagawa, M., Luo, T.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Mining and Knowledge Discovery 6, 61–82 (2002)CrossRefMathSciNetGoogle Scholar
  19. 19.
    O’Conner, M., Herlocker, J.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA (August 1999)Google Scholar
  20. 20.
    Pazzani, M.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)CrossRefGoogle Scholar
  21. 21.
    Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, C.D.: Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction 13, 311–372 (2003)CrossRefGoogle Scholar
  22. 22.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems–a case study. In: Proceedings of the WebKDD 2000 Workshop at the ACM-SIGKDD Conference on Knowledge Discovery in Databases (KDD 2000) (August 2000)Google Scholar
  23. 23.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International WWW Conference, Hong Kong (May 2001)Google Scholar
  24. 24.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommender algorithms for e-commerce. In: Proceedings of the 2nd ACM E-Commerce Conference (EC 2000), Minneapolis, MN (October 2000)Google Scholar
  25. 25.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ’word of mouth’. In: Proceedings of the Computer-Human Interaction Conference (CHI 1995), Denver, CO (May 1995)Google Scholar
  26. 26.
    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Bamshad Mobasher
    • 1
  • Xin Jin
    • 1
  • Yanzan Zhou
    • 1
  1. 1.Center for Web Intelligence, School of Computer Science, Telecommunication, and Information SystemsDePaul UniversityChicagoUSA

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