Improving retrieval accuracy by weighting document types with clickthrough data

Authors:
Peter C. K. Yeung;Charles L. A. Clarke;Stefan Büttcher
Affiliations:
University of Waterloo, Waterloo, ON, Canada;University of Waterloo, Waterloo, ON, Canada;University of Waterloo, Waterloo, ON, Canada
Venue:
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2007

Citing 3
Cited 1

Simple BM25 extension to multiple weighted fields

Proceedings of the thirteenth ACM international conference on Information and knowledge management
Modeling task-genre relationships for IR in the workplace

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improving web search ranking by incorporating user behavior information

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Automatic genre identification: towards a flexible classification scheme

FDIA'07 Proceedings of the 1st BCS IRSG conference on Future Directions in Information Access

Quantified Score

Hi-index	0.00

Visualization

Abstract

For enterprise search, there exists a relationship between work task and document type that can be used to refine search results. In this poster, we adapt the popular Okapi BM25 scoring function to weight term frequency based on the relevance of a document type to a work task. Also, we use click frequency for each task-type pair to estimate a realistic weight. Using the W3C collection from the TREC Enterprise track for evaluations, our approach leads to significant improvements on search precision.