Efficient construction of large test collections
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Minimal test collections for retrieval evaluation
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A statistical method for system evaluation using incomplete judgments
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Estimating average precision with incomplete and imperfect judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Robust test collections for retrieval evaluation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Strategic system comparisons via targeted relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Bias and the limits of pooling for large collections
Information Retrieval
A simple and efficient sampling method for estimating AP and NDCG
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation over thousands of queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Rank-biased precision for measurement of retrieval effectiveness
ACM Transactions on Information Systems (TOIS)
Efficient and effective link analysis with precomputed salsa maps
Proceedings of the 17th ACM conference on Information and knowledge management
Statistical power in retrieval experimentation
Proceedings of the 17th ACM conference on Information and knowledge management
Comparing metrics across TREC and NTCIR: the robustness to system bias
Proceedings of the 17th ACM conference on Information and knowledge management
Boiling down information retrieval test collections
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Prioritizing relevance judgments to improve the construction of IR test collections
Proceedings of the 20th ACM international conference on Information and knowledge management
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Information retrieval systems are evaluated against test collections of topics, documents, and assessments of which documents are relevant to which topics. Documents are chosen for relevance assessment by pooling runs from a set of existing systems. New systems can return unassessed documents, leading to an evaluation bias against them. In this paper, we propose to estimate the degree of bias against an unpooled system, and to adjust the system's score accordingly. Bias estimation can be done via leave-one-out experiments on the existing, pooled systems, but this requires the problematic assumption that the new system is similar to the existing ones. Instead, we propose that all systems, new and pooled, be fully assessed against a common set of topics, and the bias observed against the new system on the common topics be used to adjust scores on the existing topics. We demonstrate using resampling experiments on TREC test sets that our method leads to a marked reduction in error, even with only a relatively small number of common topics, and that the error decreases as the number of topics increases.