Two models of retrieval with probabilistic indexing
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Precision Weighting—An Effective Automatic Indexing Method
Journal of the ACM (JACM)
Operations Research Applied to Document Indexing and Retrieval Decisions
Journal of the ACM (JACM)
On the Construction of Feedback Queries
Journal of the ACM (JACM)
Effective information retrieval using term accuracy
Communications of the ACM
On the limitations of document ranking algorithms in information retrieval
SIGIR '81 Proceedings of the 4th annual international ACM SIGIR conference on Information storage and retrieval: theoretical issues in information retrieval
Evaluation of the 2-Poisson model as a basis for using term frequency data in searching
SIGIR '83 Proceedings of the 6th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Probabilistic models of indexing and searching
SIGIR '80 Proceedings of the 3rd annual ACM conference on Research and development in information retrieval
Explanation and generalization of vector models in information retrieval
SIGIR '82 Proceedings of the 5th annual ACM conference on Research and development in information retrieval
Optimum probability estimation based on expectations
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
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Most document retrieval systems based on probabilistic models of feature distributions assume random selection of documents for retrieval. The assumptions of these models are met when documents are randomly selected from the database or when retrieving all available documents. A more suitable model for retrieval of a single document assumes that the best document available is to be retrieved first. Models of document retrieval systems assuming random selection and best-first selection are developed and compared under binary independence and two Poisson independence feature distribution models. Under the best-first model, feature discrimination varies with the number of documents in each relevance class in the database. A weight similar to the Inverse Document Frequency weight and consistent with the best-first model is suggested which does not depend on knowledge of the characteristics of relevant documents.