Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
On term selection for query expansion
Journal of Documentation
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Probabilistic retrieval revisited
The Computer Journal - Special issue on information retrieval
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
Topical relevance relationships. I: why topic matching fails
Journal of the American Society for Information Science
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
ACM Computing Surveys (CSUR)
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
A unified maximum likelihood approach to document retrieval
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
Documents and queries as random variables: History and implications: Research Articles
Journal of the American Society for Information Science and Technology
Looking back: On relevance, probabilistic indexing and information retrieval
Information Processing and Management: an International Journal
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
Evaluation of system measures for incomplete relevance judgment in IR
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
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This paper proposes a new unified probabilistic model. Two previous models, Robertson et al.'s "Model 0" and "Model 3," each have strengths and weaknesses. The strength of Model 0 not found in Model 3, is that it does not require relevance data about the particular document or query, and, related to that, its probability estimates are straightforward. The strength of Model 3 not found in Model 0 is that it can utilize feedback information about the particular document and query in question. In this paper we introduce a new unified probabilistic model that combines these strengths: the expression of its probabilities is straightforward, it does not require that data must be available for the particular document or query in question, but it can utilize such specific data if it is available. The model is one way to resolve the difficulty of combining two marginal views in probabilistic retrieval.