Predicting Query Performance by Query-Drift Estimation
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
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
A unified framework for post-retrieval query-performance prediction
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Predicting Query Performance by Query-Drift Estimation
ACM Transactions on Information Systems (TOIS)
Back to the roots: a probabilistic framework for query-performance prediction
Proceedings of the 21st ACM international conference on Information and knowledge management
Query-Performance Prediction Using Minimal Relevance Feedback
Proceedings of the 2013 Conference on the Theory of Information Retrieval
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The ability to predict retrieval performance has potential applications in many important IR (Information Retrieval) areas. In this thesis, we study the problem of predicting retrieval quality at the granularity of both the retrieved document set as a whole and individual retrieved documents. At the level of ranked lists of documents, we propose several novel prediction models that capture different aspects of the retrieval process that have a major impact on retrieval effectiveness. These techniques make performance prediction both effective and efficient in various retrieval settings including a Web search environment. As an application, we also provide a framework to address the problem of query expansion prediction. At the level of documents, we predict the quality of documents in the context of Web ad-hoc retrieval. We explore document features that are predictive of quality. Furthermore, we propose a document quality language model to improve retrieval effectiveness by incorporating quality information.