iTag: a personalized blog tagger

ABSTRACT

We present iTag, a personalized tag recommendation system for blogs. iTag improves on current tag recommendation systems in two ways. First, iTag has much higher precision and recall than previously proposed tagging algorithms. For example, iTag achieved over 60% precision and recall on a set of 1000 blog posts selected at random from a WordPress RSS feed in April 2009, whereas the previously developed TagAssist achieved less than 10% precision and recall on our data. Second, iTag performs just as well when trained on a single user's blog as when trained on a large corpus of blogs. Thus, iTag can be deployed as a global, non-personalized tag recommendation system, or as a personalized tag recommender. Our experiments and survey of tagging behavior suggest that bloggers use tags idiosyncratically, so personalized tagging is an important option.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications

General Terms:
Algorithms, Experimentation, Performance

Keywords:
blogs, machine learning, tagging