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ABSTRACT

Blog feed search aims to identify a blog feed with a recurring interest in a given topic. In this paper, we investigate the "pseudo-relevance feedback" for blog feed search task, where its unit of relevance judgment is not based on a blog post but a blog feed (the collection of all its constituent posts). This paper focuses on two characteristics of feed search task, blog feed's topical diversity and multifaceted property of query. We propose a novel feed-level selection of local posts which uses only highly relevant local posts in each top-ranked feed, in order to capture the correct and diverse relevant information to a given topic. Experimental results show that the proposed approach outperforms traditional feedback approaches. Especially, the proposed approach gives 2% further increase of nDCG over the best performing result of TREC '08 Blog Distillation Task.

REFERENCES

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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.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

General Terms:
Algorithms, Experimentation, Performance

Keywords:
blog distillation, feed search, pseudo-relevance feedback