Improving document ranking in information retrieval using ordered weighted aggregation and leximin refinement

Mohand Boughanem (1) , Yannick Loiseau (1, 2) , Henri Prade (3)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes
3 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage

Classical information retrieval (IR) methods often lose valuable information when aggregating weights, which may diminish the discriminating power between documents. To cope with this problem, the paper presents an approach for ranking documents in IR, based on a vector-based ordering technique already considered in fuzzy logic for multiple criteria analysis purpose. Moreover, the proposed approach uses a possibilistic framework for encoding the retrieval status values. The approach, applied to a benchmark collection, has been shown to improve IR precision w.r.t. classical approaches.

Mots clés: SIGRI

Domaines: Intelligence artificielle [cs.AI]

Mohand Boughanem, Yannick Loiseau, Henri Prade. Improving document ranking in information retrieval using ordered weighted aggregation and leximin refinement. 4th Conference of the European Society for Fuzzy Logic and Technology and 11èmes Rencontres Francophones sur la Logique Floue et ses Applications (EUSFLAT LFA 2005), Sep 2005, Barcelona, Espagne. pp.1269-1274. ⟨hal-03367402⟩