### Abstract

Original language | English |
---|---|

Title of host publication | Advances in Information Retrieval Theory |

Subtitle of host publication | Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings |

Editors | Giambattista Amati, Fabio Crestani |

Place of Publication | Berlin, Heidelberg |

Publisher | Springer |

Pages | 164-175 |

Number of pages | 12 |

ISBN (Electronic) | 978-3-642-23318-0 |

ISBN (Print) | 978-3-642-23317-3 |

DOIs | |

Publication status | Published - Sep 2011 |

Event | 3rd International Conference on Advances in Information Retrieval Theory, ICTIR 2011 - Bertinoro, Italy Duration: 12 Sep 2011 → 14 Sep 2011 Conference number: 3 |

### Publication series

Name | Lecture Notes in Computer Science |
---|---|

Publisher | Springer Verlag |

Volume | 6931 |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Conference

Conference | 3rd International Conference on Advances in Information Retrieval Theory, ICTIR 2011 |
---|---|

Abbreviated title | ICTIR |

Country | Italy |

City | Bertinoro |

Period | 12/09/11 → 14/09/11 |

### Fingerprint

### Keywords

- METIS-278719
- EWI-20202
- IR-78116

### Cite this

*Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings*(pp. 164-175). (Lecture Notes in Computer Science; Vol. 6931). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-23318-0_16

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*Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings.*Lecture Notes in Computer Science, vol. 6931, Springer, Berlin, Heidelberg, pp. 164-175, 3rd International Conference on Advances in Information Retrieval Theory, ICTIR 2011, Bertinoro, Italy, 12/09/11. https://doi.org/10.1007/978-3-642-23318-0_16

**Towards a Better Understanding of the Relationship Between Probabilistic Models in IR.** / Aly, Robin; Demeester, Thomas.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Towards a Better Understanding of the Relationship Between Probabilistic Models in IR

AU - Aly, Robin

AU - Demeester, Thomas

PY - 2011/9

Y1 - 2011/9

N2 - Probability of relevance (PR) models are generally assumed to implement the Probability Ranking Principle (PRP) of IR, and recent publications claim that PR models and language models are similar. However, a careful analysis reveals two gaps in the chain of reasoning behind this statement. First, the PRP considers the relevance of particular documents, whereas PR models consider the relevance of any query-document pair. Second, unlike PR models, language models consider draws of terms and documents. We bridge the first gap by showing how the probability measure of PR models can be used to define the probabilistic model of the PRP. Furthermore, we argue that given the differences between PR models and language models, the second gap cannot be bridged at the probabilistic model level. We instead define a new PR model based on logistic regression, which has a similar score function to the one of the query likelihood model. The performance of both models is strongly correlated, hence providing a bridge for the second gap at the functional and ranking level. Understanding language models in relation with logistic regression models opens ample new research directions which we propose as future work.

AB - Probability of relevance (PR) models are generally assumed to implement the Probability Ranking Principle (PRP) of IR, and recent publications claim that PR models and language models are similar. However, a careful analysis reveals two gaps in the chain of reasoning behind this statement. First, the PRP considers the relevance of particular documents, whereas PR models consider the relevance of any query-document pair. Second, unlike PR models, language models consider draws of terms and documents. We bridge the first gap by showing how the probability measure of PR models can be used to define the probabilistic model of the PRP. Furthermore, we argue that given the differences between PR models and language models, the second gap cannot be bridged at the probabilistic model level. We instead define a new PR model based on logistic regression, which has a similar score function to the one of the query likelihood model. The performance of both models is strongly correlated, hence providing a bridge for the second gap at the functional and ranking level. Understanding language models in relation with logistic regression models opens ample new research directions which we propose as future work.

KW - METIS-278719

KW - EWI-20202

KW - IR-78116

U2 - 10.1007/978-3-642-23318-0_16

DO - 10.1007/978-3-642-23318-0_16

M3 - Conference contribution

SN - 978-3-642-23317-3

T3 - Lecture Notes in Computer Science

SP - 164

EP - 175

BT - Advances in Information Retrieval Theory

A2 - Amati, Giambattista

A2 - Crestani, Fabio

PB - Springer

CY - Berlin, Heidelberg

ER -