Tag-based object similarity computation using term space dimension reduction

ABSTRACT

In this paper, we propose a novel approach for measuring similarity between web objects. Our similarity measure is defined based on the representation of a web object as a collection of tags. Precisely, we first construct a vector space in which multiple terms are mapped into a single dimension by using information available from Open Directory Project and Delicious.com. Then we position web objects in the vector space and apply the traditional cosine measure for similarity computation. We demonstrate that the proposed similarity computation method is able to overcome the limitation of traditional vector space approach while at the same time require less computational cost compares to LSI (Latent Semantic Indexing).

REFERENCES

• 1: Taher H. Haveliwala , Aristides Gionis , Dan Klein , Piotr Indyk, Evaluating strategies for similarity search on the web, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA [doi>10.1145/511446.511502]
• 2: Christopher D. Manning , Prabhakar Raghavan , Hinrich Schtze, Introduction to Information Retrieval, Cambridge University Press, New York, NY, 2008
• 3: Shirky, C., Ontology is Overrated: Categories, Links and Tags. http://www.shirky.com

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
open directory project, similarity, vector space model, web search