A general language model for information retrieval (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th 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
Precision prediction based on ranked list coherence
Information Retrieval
Estimating average precision with incomplete and imperfect judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Query performance prediction in web search environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval performance prediction and document quality
Retrieval performance prediction and document quality
Information Retrieval
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
An implicit feedback approach for interactive information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Using statistical decision theory and relevance models for query-performance prediction
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Estimating the Query Difficulty for Information Retrieval
Estimating the Query Difficulty for Information Retrieval
A unified framework for post-retrieval query-performance prediction
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Document Score Distribution Models for Query Performance Inference and Prediction
ACM Transactions on Information Systems (TOIS)
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There has been much work on devising query-performance prediction approaches that estimate search effectiveness without relevance judgments (i.e., zero feedback). Specifically, post-retrieval predictors analyze the result list of top-retrieved documents. Departing from the zero-feedback approach, in this paper we show that relevance feedback for even very few top ranked documents can be exploited to dramatically improve prediction quality. Specifically, applying state-of-the-art zero-feedback-based predictors to only a very few relevant documents, rather than to the entire result list as originally designed, substantially improves prediction quality. This novel form of prediction is based on quantifying properties of relevant documents that can attest to query performance. We also show that integrating prediction based on relevant documents with zero-feedback-based prediction is highly effective; specifically, with respect to utilizing state-of-the-art direct estimates of retrieval effectiveness when minimal feedback is available.