Relevance as Resonance: A New Theoretical Perspective and a Practical Utilization in Information Filtering

Abstract : This paper presents a new adaptive filtering system called RELIEFS. This system is based on neural mechanisms underlying an information selection process. It is inspired from the cognitive model adaptive resonance theory [Biol. Cybernet. 23 (1976) 121] that proposes a neural explanation of how our brain selects information from its environment. In our approach, resonance, the key idea of this model is used to model the notion of relevance in information retrieval and information filtering (IF). The comparison of resonance with the previous models of relevance shows that resonance captures the very core of most existing models. Moreover, the notion of resonance provides a new angle to look at relevance and opens new theoretical perspectives. The proposed mechanism based on resonance has been directly implemented and tested on the TREC-9 and TREC-11 IF data. The experimental results show that this approach can result in a high effectiveness in practice.
Type de document :
Article dans une revue
Information Processing and Management, Elsevier, 2004, 40 (1), pp.1--19
Liste complète des métadonnées

https://hal.inria.fr/hal-00953806
Contributeur : Marie-Christine Fauvet <>
Soumis le : vendredi 28 février 2014 - 16:01:28
Dernière modification le : jeudi 11 janvier 2018 - 06:27:15

Identifiants

  • HAL Id : hal-00953806, version 1

Collections

Citation

Christophe Brouard, Jian-Yun Nie. Relevance as Resonance: A New Theoretical Perspective and a Practical Utilization in Information Filtering. Information Processing and Management, Elsevier, 2004, 40 (1), pp.1--19. 〈hal-00953806〉

Partager

Métriques

Consultations de la notice

60