IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation by highly relevant documents
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
One Class Classification Methods Based Non-Relevance Feedback Document Retrieval
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Estimation and use of uncertainty in pseudo-relevance feedback
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Towards robust query expansion: model selection in the language modeling framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion with the minimum relevance judgments
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Improving retrievability with improved cluster-based pseudo-relevance feedback selection
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-ranked documents are selected as feedback to build a new expansion query model. However, very little attention has been paid to an intuitive but critical fact that the retrieval performance for different queries is sensitive to the selection of different numbers of feedback documents. In this paper, we explore two approaches to incorporate the factor of query-specific feedback document selection in an automatic way. The first is to determine the "optimal" number of feedback documents with respect to a query by adopting the clarity score and cumulative gain. The other approach is that, instead of capturing the optimal number, we hope to weaken the effect of the numbers of feedback document, i.e., to improve the robustness of the pseudo relevance feedback process, by a mixture model. Our experimental results show that both approaches improve the overall retrieval performance.