Resource Recommendation in Collaborative Tagging Applications

  • Jonathan Gemmell
  • Thomas Schimoler
  • Bamshad Mobasher
  • Robin Burke
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 61)


Collaborative tagging applications enable users to annotate online resources with user-generated keywords. The collection of these annotations and the way they connect users and resources produce a rich information space for users to explore. However the size, complexity and chaotic structure of these systems hamper users as they search for information. Recommenders can assist the user by suggesting resources, tags or even other users. Previous work has demonstrated that an integrative approach which exploits all three dimensions of the data (users, resources, tags) produce superior results in tag recommendation. We extend this integrative philosophy to resource recommendation. Specifically, we propose an approach for designing weighted linear hybrid resource recommenders. Through extensive experimentation on two large real world datasets, we show that the hybrid recommenders surpass the effectiveness of their constituent components while inheriting their simplicity, computational efficiency and explanatory capacity. We further introduce the notion of information channels which describe the interaction of the three dimensions. Information channels can be used to explain the effectiveness of individual recommenders or explain the relative contribution of components in the hybrid recommender.


Collaborative Tagging Information Channel Hybrid Recommender 


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  1. 1.
    Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefGoogle Scholar
  2. 2.
    Deshpande, M., Karypis, G.: Item-Based Top-N Recommendation Algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004)CrossRefGoogle Scholar
  3. 3.
    Gemmell, J., Ramezani, M., Schimoler, T., Christiansen, L., Mobasher, B.: A Fast Effective Multi-Channeled Tag Recommender. In: ECML/PKDD 2009 Discovery Challenge Workshop, Part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 59–63 (2009)Google Scholar
  4. 4.
    Gemmell, J., Schimoler, T., Ramezani, M., Christiansen, L., Mobasher, B.: Improving FolkRank With Item-Based Collaborative Filtering. Recommender Systems & the Social Web (2009)Google Scholar
  5. 5.
    Gemmell, J., Shepitsen, A., Mobasher, B., Burke, R.: Personalization in Folksonomies Based on Tag Clustering. Intelligent Techniques for Web Personalization & Recommender Systems (2008)Google Scholar
  6. 6.
    Gemmell, J., Shepitsen, A., Mobasher, B., Burke, R.: Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 196–205. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 237. ACM, New York (1999)Google Scholar
  8. 8.
    Hotho, A., Jaschke, R., Schmitz, C., Stumme, G.: Information Retrieval in Folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    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), 87 (1997)CrossRefGoogle Scholar
  10. 10.
    Mathes, A.: Folksonomies-Cooperative Classification and Communication Through Shared Metadata. In: Computer Mediated Communication (Doctoral Seminar), Graduate School of Library and Information Science, University of Illinois Urbana-Champaign (December 2004)Google Scholar
  11. 11.
    Mika, P.: Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5(1), 5–15 (2007)CrossRefGoogle Scholar
  12. 12.
    Rendle, S., Schmidt-Thieme, L.: Factor Models for Tag Recommendation in BibSonomy. In: ECML/PKDD 2008 Discovery Challenge Workshop, Part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 235–243 (2009)Google Scholar
  13. 13.
    Rendle, S., Schmidt-Thieme, L.: Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 81–90. ACM, New York (2010)Google Scholar
  14. 14.
    Salton, G., Wong, A., Yang, C.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975)CrossRefGoogle Scholar
  15. 15.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: 10th International Conference on World Wide Web, p. 295. ACM, New York (2001)Google Scholar
  16. 16.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating Word of Mouth. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co., New York (1995)Google Scholar
  17. 17.
    Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized Recommendation in Social Tagging Systems using Hierarchical Clustering. In: ACM Conference on Recommender Systems, pp. 259–266. ACM, New York (2008)CrossRefGoogle Scholar
  18. 18.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM conference on Recommender systems, pp. 43–50. ACM, New York (2008)CrossRefGoogle Scholar
  19. 19.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis. IEEE Transactions on Knowledge and Data Engineering (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jonathan Gemmell
    • 1
  • Thomas Schimoler
    • 1
  • Bamshad Mobasher
    • 1
  • Robin Burke
    • 1
  1. 1.Center for Web Intelligence, School of ComputingDePaul UniversityChicagoUSA

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