A study of methods for negative relevance feedback

Xuanhui Wang, Hui Fang, Cheng Xiang Zhai

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

Abstract

Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedback methods are generally more effective than those based on vector-space models, and using multiple negative models is an effective heuristic for negative feedback. Our results also show that it is feasible to adapt test collections with easy topics for evaluating negative feedback methods through sampling.

Original languageEnglish (US)
Title of host publicationACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings
Pages219-226
Number of pages8
DOIs
StatePublished - Dec 15 2008
Event31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008 - Singapore, Singapore
Duration: Jul 20 2008Jul 24 2008

Publication series

NameACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings

Other

Other31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008
CountrySingapore
CitySingapore
Period7/20/087/24/08

Keywords

  • Difficult topics
  • Language models
  • Negative feedback
  • Vector space models

ASJC Scopus subject areas

  • Information Systems
  • Software

Fingerprint Dive into the research topics of 'A study of methods for negative relevance feedback'. Together they form a unique fingerprint.

Cite this