Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in 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
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
Retrieval of complex objects using a four-valued logic
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
DOLORES: a system for logic-based retrieval of multimedia objects
Proceedings of the 21st annual international ACM SIGIR conference on Research and development 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
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
HySpirit - A Probabilistic Inference Engine for Hypermedia Retrieval in Large Databases
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
An information-theoretic perspective of tf—idf measures
Information Processing and Management: an International Journal
A frequency-based and a poisson-based definition of the probability of being informative
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Geometry of Information Retrieval
The Geometry of Information Retrieval
Combining the language model and inference network approaches to retrieval
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
On Event Spaces and Probabilistic Models in Information Retrieval
Information 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
An exploration of axiomatic approaches to information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A parallel derivation of probabilistic information retrieval models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
TF-IDF uncovered: a study of theories and probabilities
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
On event space and rank equivalence between probabilistic retrieval models
Information Retrieval
Semi-subsumed Events: A Probabilistic Semantics of the BM25 Term Frequency Quantification
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
A general matrix framework for modelling Information Retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Towards a better understanding of the relationship between probabilistic models in IR
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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In IR research it is essential to know IR models. Research over the past years has consolidated the foundations of IR models. Moreover, relationships have been reported that help to use and position IR models. Knowing about the foundations and relationships of IR models can significantly improve building information management systems. The first part of this tutorial presents an in-depth consolidation of the foundations of the main IR models (TF-IDF, BM25, LM). Particular attention will be given to notation and probabilistic roots. The second part crystallises the relationships between models. Does LM embody IDF? How "heuristic" is TF-IDF? What are the probabilistic roots? How are LM and the probability of relevance related? What are the components shared by the main IR models? After the tutorial, attendees will be familiar with a consolidated view on IR models. The tutorial will be illustrative and interactive, providing opportunities to exchange controversial issues and research challenges.