Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, web links, products etc.). Social tagging systems (STSs) can recommend users with common social interest based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a model to capture the three types of entities that exist in a social tagging system: users, items, and tags. These data are represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) method. We perform experimental comparison of the proposed method against a baseline user recommendation algorithm with a real data set (BibSonomy), attaining significant improvements.


Recommender System Latent Semantic Analysis Target User Interesting User Recommendation Algorithm 
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.


  1. 1.
    N. Ali-Hasan and A. Adamic. Expressing social relationships on the blog through links and comments. In Proceedings ICWSM Conference, 2007.Google Scholar
  2. 2.
    S. Chen, F. Wang, and C. Zhang. Simultaneous heterogeneous data clustering based on higher order relationships. In Proceedings Workshop on Mining Graphs and Complex Structures (MGCS'07), in conjunction with ICDM'07, pages 387–392.Google Scholar
  3. 3.
    A. Hotho, R. Jaschke, C. Schmitz, and G. Stumme. Information retrieval in folk-sonomies: Search and ranking. In The Semantic Web: Research and Applications, pages 411–426, 2006.Google Scholar
  4. 4.
    L. de Lathauwer, B. de Moor, and J. Vandewalle. A multilinear singular value decomposition. SIAM Journal of Matrix Analysis and Applications, 21(4):1253–1278, 2000.MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    X. Li, L. Guo, and Y. Zhao. Tag-based social interest discovery. In Proceedings ACM WWW Conference, 2008.Google Scholar
  6. 6.
    K. Sripanidkulchai, B. Maggs, and H. Zhang. Efficient content location using interest-based locality in peer-to-peer systems. In Proceedings INFOCOM Conference, 2003.Google Scholar
  7. 7.
    J. Sun, D. Shen, H. Zeng, Q. Yang, Y. Lu, and Z. Chen. Cubesvd: a novel approach to personalized web search. In Proceedings World Wide Web Conference, pages 382–390, 2005.Google Scholar
  8. 8.
    H. Wang and N. Ahuja. A tensor approximation approach to dimensionality reduction. International Journal of Computer Vision, 2007.Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Panagiotis Symeonidis
    • 1
  1. 1.Department of InformaticsAristotle UniversityThessalonikiGreece

Personalised recommendations