A Visual Analysis of the Effects of Assumptions of Classical Probabilistic Models

Authors:
Emanuele Di Buccio;Giorgio Maria Di Nunzio
Affiliations:
Department of Information Engineering, University of Padua, Italy;Department of Information Engineering, University of Padua, Italy
Venue:
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Year:
2013

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Abstract

This poster discusses the main assumptions of classical probabilistic models in IR by means of a visual data analysis approach. Starting from the problem of classification of documents into relevant and non relevant classes, we derive the exact same formula of the relevance weight of the Binary Independence Model but with more degrees of interaction. With this approach, new factors can be taken into account to obtain a different ranking of the documents.