A boosting approach to improving pseudo-relevance feedback

Yuanhua Lv, Cheng Xiang Zhai, Wan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback helps many queries, it also often hurts many other queries, limiting its usefulness in real retrieval applications. Thus an important, yet difficult challenge is to improve the overall effectiveness of pseudo-relevance feedback without sacrificing the performance of individual queries too much. In this paper, we propose a novel learning algorithm, FeedbackBoost, based on the boosting framework to improve pseudo-relevance feedback through optimizing the combination of a set of basis feedback algorithms using a loss function defined to directly measure both robustness and effectiveness. FeedbackBoost can potentially accommodate many basis feedback methods as features in the model, making the proposed method a general optimization framework for pseudo-relevance feedback. As an application, we apply FeedbackBoost to improve pseudo feedback based on language models through combining different document weighting strategies. The experiment results demonstrate that FeedbackBoost can achieve better average precision and meanwhile dramatically reduce the number and magnitude of feedback failures as compared to three representative pseudo feedback methods and a standard learning to rank approach for pseudo feedback.

Original languageEnglish (US)
Title of host publicationSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages165-174
Number of pages10
ISBN (Print)9781450309349
DOIs
StatePublished - Jan 1 2011
Event34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011 - Beijing, China
Duration: Jul 24 2011Jul 28 2011

Publication series

NameSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
CountryChina
CityBeijing
Period7/24/117/28/11

Keywords

  • Boosting
  • FeedbackBoost
  • Learning
  • Loss function
  • Optimization
  • Pseudo-relevance feedback
  • Robustness

ASJC Scopus subject areas

  • Information Systems

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