Improving Collaborative Filtering in Social Tagging Systems

  • Felice Ferrara
  • Carlo Tasso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7023)

Abstract

User-based Collaborative Filtering (CF) systems generate recommendations for a specific user by combining feedback (i.e. information about what is relevant for a user) provided by a set of people similar to that user. In these system the similarity among people is computed by taking into account the set of shared resources. However, there are several application domains, such as social tagging systems, where each user may have several different Topic of Interests (ToIs). In these cases, two users could share only some interests and, therefore, only a part of the feedback should be considered for producing recommendations. Focusing on social tagging systems, we propose here a novel approach to detect ToIs in the collection of the bookmarks of a user. Given a specific ToI, we adaptively identify similar people (i.e., sharing the same ToI) and select only the resources relevant to the specific ToI.

Keywords

Social tagging collaborative filtering adaptive system personalization 

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References

  1. 1.
    Zanardi, V., Capra, L.: Social ranking: Finding relevant content in web 2.0. In: Proc. of the 2nd ACM Int. Conf. on Recommender Systems, Lausanne, Switzerland (2008)Google Scholar
  2. 2.
    Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations. In: Proceedings of the 1st International Workshop on Collective Semantics: Collective Intelligence and the Semantic Web (CISWeb 2008), pp. 5–19 (2008)Google Scholar
  3. 3.
    Dattolo, A., Ferrara, F., Tasso, C.: On social semantic relations for recommending tags and resources using folksonomies. In: Human-Computer Systems Interaction. Backgrounds and Applications 2Google Scholar
  4. 4.
    Dattolo, A., Ferrara, F., Tasso, C.: Supporting Personalized user Concept Spaces and Recommendations for a Publication Sharing System. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 325–330. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Zhou, T., Ma, H., Lyu, M., King, I.: Userrec: A user recommendation framework in social tagging systems. In: Proc. of the 24th AAAI Conf., Atlanta, Geogia, USA, pp. 1486–1491 (2010)Google Scholar
  6. 6.
    Baltrunas, L., Ricci, F.: Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 22–31. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11 (2009)Google Scholar
  8. 8.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32 (2010)Google Scholar
  9. 9.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. Transaction on Information Systems 1, 143–177 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Felice Ferrara
    • 1
  • Carlo Tasso
    • 1
  1. 1.University of UdineUdineItaly

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