Improving Social Filtering Techniques Through WordNet-Based User Profiles

  • Pasquale Lops
  • Marco Degemmis
  • Giovanni Semeraro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


Collaborative filtering algorithms predict the preferences of a user for an item by weighting the contributions of similar users, called neighbors, for that item. Similarity between users is computed by comparing their rating styles, i.e. the set of ratings given on the same items. Unfortunately, similarity between users is computable only if they have common rated items. The main contribution of this paper is a (content-collaborative) hybrid recommender system which overcomes this limitation by computing similarity between users on the ground of their content-based profiles. Traditional keyword-based profiles are unable to capture the semantics of user interests, due to the natural language ambiguity. A distinctive feature of the proposed technique is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in the WordNet lexical database. This model, called the semantic user profile, is exploited by the hybrid recommender in the neighborhood formation process. The results of an experimental session in a movie recommendation scenario demonstrate the effectiveness of the proposed approach.


Collaborative Filter Mean Absolute Error User Interest Similar User Word Sense Disambiguation 
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.
    Asnicar, F., Tasso, C.: ifweb: A Prototype of User Model-based Intelligent Agent for Documentation Filtering and Navigation in the Word Wide Web. In: Tasso, C., Jameson, A., Paris, C.L. (eds.) Proc. of the 1st Int. Workshop on Adaptive Systems and User Modeling on the WWW, pp. 3–12 (1997)Google Scholar
  2. 2.
    Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  3. 3.
    Bloedhorn, S., Hotho, A.: Boosting for text classification with semantic features. In: Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Mining for and from the Semantic Web Workshop, pp. 70–87 (2004)Google Scholar
  4. 4.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User. Modeling and User.-Adapted Interaction 12(4), 331–370 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Degemmis, M., Lops, P., Semeraro, G.: Learning Semantic User Profiles from Text. In: Advanced Data Mining and Applications, Proc. of the 2nd Int. Conf., Xi’an, China, pp. 661–672. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Degemmis, M., Lops, P., Semeraro, G.: A Content-collaborative Recommender that Exploits Wordnet-based User Profiles for Neighborhood Formation. User Modeling and User-Adapted Interaction (forthcoming, 2007)Google Scholar
  7. 7.
    Hartigan, J.: Clustering Algorithms. John Wiley & Sons, Chichester (1975)zbMATHGoogle Scholar
  8. 8.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proc. of the 22nd Annual Int. ACM SIGIR Conference on Research and Development in Information Retrieval. Theoretical Models, pp. 230–237. ACM Press, New York (1999)CrossRefGoogle Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Leacock, C., Chodorow, M.: Combining Local Context and WordNet Similarity for Word Sense Identification. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 266–283. MIT Press, Cambridge (1998)Google Scholar
  11. 11.
    Magnini, B., Strapparava, C.: Improving User Modelling with Content-based Techniques. In: Proc. of the 8th Int. Conf. on User Modeling, Sonthofen, Germany, pp. 74–83. Springer, Heidelberg (2001)Google Scholar
  12. 12.
    Massa, P.: Trust-aware Decentralized Recommender Systems. PhD thesis, International Doctorate School in Information and Communication Technologies, University of Trento (2006)Google Scholar
  13. 13.
    Mavroeidis, D., Tsatsaronis, G., Vazirgiannis, M., Theobald, M., Weikum, G.: word sense disambiguation for exploiting hierarchical thesauri in text classification. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 181–192. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted Collaborative Filtering for Improved Recommendations. In: Proc. of the 18th National Conf. on Artificial Intelligence and 14th Conf. on Innovative Applications of Artificial Intelligence (AAAI/IAAI-02), Menlo Parc, pp. 187–192. AAAI Press, Stanford, California (2002)Google Scholar
  15. 15.
    Miller, G.: Wordnet: An On-line Lexical Database (Special Issue). International Journal of Lexicography 3(4), 235–312 (1990)CrossRefGoogle Scholar
  16. 16.
    Pazzani, M.J.: A Framework for Collaborative, Content-based and Demographic Filtering. Artificial Intelligence Review 13(5-6), 393–408 (1999)CrossRefGoogle Scholar
  17. 17.
    Sarwar, B.M., Karypis, G., Konstan, J., Reidl, J.: Recommender Systems for Large-scale e-commerce: Scalable Neighborhood Formation Using Clustering. In: Proc. of the 5th Int. Conf. on Computer and Information Technology (2002)Google Scholar
  18. 18.
    Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  19. 19.
    Semeraro, G., Degemmis, M., Lops, P., Basile, P.: Combining Learning and Word Sense Disambiguation for Intelligent User Profiling. In: Proc. of the 20th Int. Joint Conf. on Artificial Intelligence 2007, Hyderabad, India, pp. 2856–2861 (2007)Google Scholar
  20. 20.
    Ungar, L.H., Foster, D.P.: Clustering Methods for Collaborative Filtering. In: Proc. of the Workshop on Recommendation Systems, AAAI Press, Stanford, California (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Pasquale Lops
    • 1
  • Marco Degemmis
    • 1
  • Giovanni Semeraro
    • 1
  1. 1.Department of Informatics - University of BariItaly

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