A new probabilistic ranking model

Richard Connor, Robert Moss, Morgan Harvey

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)
74 Downloads (Pure)


Over the years a number of models have been introduced as solutions to the central IR problem of ranking documents given textual queries. Here we define another new model. It is a probabilistic model and has no term inter-dependencies, thus allowing calculation from inverted indices. It is based upon a simple core hypothesis, directly calculating a ranking score in terms of probability theory. Early results show that its performance is credible, even in the absence of parameters or heuristics. Its semantic basis gives absolute results, allowing different rankings to be compared with each other. The investigation of this model is at a very early stage; here, we simply propose the model for further investigation.
Original languageEnglish
Title of host publicationProceeding ICTIR '13 Proceedings of the 2013 Conference on the Theory of Information Retrieval
EditorsOrlen Kurland, Donald Metzler, Christina Lioma, Birger Larsen, Peter Ingwersen
Place of PublicationNew York, NY
Number of pages4
Publication statusPublished - 29 Sep 2013
Event2013 Conference on the Theory of Information Retrieval - Copenhagen, Denmark
Duration: 29 Sep 20132 Oct 2013


Conference2013 Conference on the Theory of Information Retrieval


  • information retrieval
  • ranking documents
  • probabilistic retrieval
  • Jensen-Shannon divergence
  • textual queries


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