Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The probability ranking principle in IR
Readings in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st 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
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
Bayesian extension to the language model for ad hoc information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Relevance information: a loss of entropy but a gain for IDF?
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Score standardization for inter-collection comparison of retrieval systems
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Estimating retrieval effectiveness using rank distributions
Proceedings of the 17th ACM conference on Information and knowledge management
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Reducing the uncertainty in resource selection
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
| Hi-index | 0.00 |
Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document's rank. Further, such models seldom have an explicit utility (or cost) that is optimized when ranking documents. To address these issues, we take a Bayesian perspective that explicitly considers the uncertainty associated with the estimation of the probability of relevance, and propose an asymmetric cost function for document ranking. Our cost function has the advantage of adjusting the risk in document retrieval via a single parameter for any probabilistic retrieval model. We use the logit model to transform the document posterior distribution with probability space [0,1] into a normal distribution with variable space ( *** *** , + *** ). We apply our risk adjustment approach to a language modelling framework for risk adjustable document ranking. Our experimental results show that our risk-aware model can significantly improve the performance of language models, both with and without background smoothing. When our method is applied to a language model without background smoothing, it can perform as well as the Dirichlet smoothing approach.