An improved feedback approach using relevant local posts for blog feed retrieval

  • Authors:

  • Yeha Lee;Seung-Hoon Na;Jong-Hyeok Lee

  • Affiliations:

  • Pohang University of Science and Technology, Pohang, South Korea;National University of Singapore, Singapore, Singapore;Pohang University of Science and Technology, Pohang, South Korea

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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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.