A probabilistic solution to the selection and fusion problem in distributed information retrieval
Proceedings of the 22nd 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
The score-distributional threshold optimization for adaptive binary classification tasks
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Maximum likelihood estimation for filtering thresholds
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Using asymmetric distributions to improve text classifier probability estimates
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A geometric interpretation and analysis of R-precision
Proceedings of the 14th ACM international conference on Information and knowledge management
Where to stop reading a ranked list?: threshold optimization using truncated score distributions
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
On score distributions and relevance
ECIR'07 Proceedings of the 29th European conference on IR research
Variational bayes for modeling score distributions
Information Retrieval
Predicting Query Performance by Query-Drift Estimation
ACM Transactions on Information Systems (TOIS)
Measuring the ability of score distributions to model relevance
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Predicting query performance directly from score distributions
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Extended expectation maximization for inferring score distributions
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
On the inference of average precision from score distributions
Proceedings of the 21st ACM international conference on Information and knowledge management
Taily: shard selection using the tail of score distributions
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Copulas for information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Modelling Score Distributions Without Actual Scores
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Document Score Distribution Models for Query Performance Inference and Prediction
ACM Transactions on Information Systems (TOIS)
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Inferring the score distribution of relevant and non-relevant documents is an essential task for many IR applications (e.g. information filtering, recall-oriented IR, meta-search, distributed IR). Modeling score distributions in an accurate manner is the basis of any inference. Thus, numerous score distribution models have been proposed in the literature. Most of the models were proposed on the basis of empirical evidence and goodness-of-fit. In this work, we model score distributions in a rather different, systematic manner. We start with a basic assumption on the distribution of terms in a document. Following the transformations applied on term frequencies by two basic ranking functions, BM25 and Language Models, we derive the distribution of the produced scores for all documents. Then we focus on the relevant documents. We detach our analysis from particular ranking functions. Instead, we consider a model for precision-recall curves, and given this model, we present a general mathematical framework which, given any score distribution for all retrieved documents, produces an analytical formula for the score distribution of relevant documents that is consistent with the precision-recall curves that follow the aforementioned model. In particular, assuming a Gamma distribution for all retrieved documents, we show that the derived distribution for the relevant documents resembles a Gaussian distribution with a heavy right tail.