A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th 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
Information Retrieval
Risky business: modeling and exploiting uncertainty in information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Adaptive relevance feedback in information retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
A unified optimization framework for robust pseudo-relevance feedback algorithms
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Bias-variance analysis in estimating true query model for information retrieval
Information Processing and Management: an International Journal
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Estimating the probability of relevance for a document is fundamental in information retrieval. From a theoretical point of view, risk exists in the estimation process, in the sense that the estimated probabilities may not be the actual ones precisely. The estimation risk is often considered to be dependent on the rank. For example, the probability ranking principle assumes that ranking documents in the order of decreasing probability of relevance can optimize the rank effectiveness. This implies that a precise estimation can yield an optimal rank. However, an optimal (or even ideal) rank does not always guarantee that the estimated probabilities are precise. This means that part of the estimation risk is rank-independent. It imposes practical risks in the applications, such as pseudo relevance feedback, where different estimated probabilities of relevance in the first-round retrieval will make a difference even when two ranks are identical. In this paper, we will explore the effect and the modeling of such rank-independent risk. A risk management method is proposed to adaptively adjust the rank-independent risk. Experimental results on several TREC collections demonstrate the effectiveness of the proposed models for both pseudo-relevance feedback and relevance feedback.