Probabilistic and genetic algorithms in document retrieval
Communications of the ACM
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
ACM Transactions on Information Systems (TOIS)
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic indexing is an optimal special case of multidimensional scaling
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic retrieval based on staged logistic regression
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Modelling documents with multiple Poisson distributions
Information Processing and Management: an International Journal
The formalism of probability theory in IR: a foundation or an encumbrance?
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Representing documents using an explicit model of their similarities
Journal of the American Society for Information Science
The effect of accessing nonmatching documents on relevance feedback
ACM Transactions on Information Systems (TOIS)
Beyond search: the information access research group at Apple
ACM SIGCHI Bulletin
Optimizing similarity using multi-query relevance feedback
Journal of the American Society for Information Science
A re-unification of two competing models for document retrieval
Journal of the American Society for Information Science - Special topic issue: youth issues in information science
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Improvement of vector space information retrieval model based on supervised learning
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
A new unified probabilistic model
Journal of the American Society for Information Science and Technology
Relevance models to help estimate document and query parameters
ACM Transactions on Information Systems (TOIS)
Improving document representations using relevance feedback: the RFA algorithm
Proceedings of the thirteenth ACM international conference on Information and knowledge management
A rough-fuzzy document grading system for customized text information retrieval
Information Processing and Management: an International Journal
Documents and queries as random variables: History and implications: Research Articles
Journal of the American Society for Information Science and Technology
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Empirical work shows significant benefits from using relevance feedback data to improve information retrieval (IR) performance. Still, one fundamental difficulty has limited the ability to fully exploit this valuable data. The problem is that it is not clear whether the relevance feedback data should be used to train the system about what the users really mean, or about what the documents really mean. In this paper, we resolve the question using a maximum likelihood framework. We show how all the available data can be used to simultaneously estimate both documents and queries in proportions that are optimal in a maximum likelihood sense. The resulting algorithm is directly applicable to many approaches to IR, and the unified framework can help explain previously reported results as well as guide the search for new methods that utilize feedback data in IR.