Retrieval and feedback models for blog feed search

  • Authors:

  • Jonathan L. Elsas;Jaime Arguello;Jamie Callan;Jaime G. Carbonell

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2008

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Abstract

Blog feed search poses different and interesting challenges from traditional ad hoc document retrieval. The units of retrieval, the blogs, are collections of documents, the blog posts. In this work we adapt a state-of-the-art federated search model to the feed retrieval task, showing a significant improvement over algorithms based on the best performing submissions in the TREC 2007 Blog Distillation task[12]. We also show that typical query expansion techniques such as pseudo-relevance feedback using the blog corpus do not provide any significant performance improvement and in many cases dramatically hurt performance. We perform an in-depth analysis of the behavior of pseudo-relevance feedback for this task and develop a novel query expansion technique using the link structure in Wikipedia. This query expansion technique provides significant and consistent performance improvements for this task, yielding a 22% and 14% improvement in MAP over the unexpanded query for our baseline and federated algorithms respectively.