Exploiting Multi-Evidence from Multiple User's Interests to Personalizing Information Retrieval

Lynda Tamine (1, 2) , Mohand Boughanem (1, 2) , Nesrine Zemirli (1)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 UT3 - Université Toulouse III - Paul Sabatier

The goal of personalization in information retrieval is to tailor the search engine results to the specific goals, preferences and general interests of the users. We propose a novel model that considers the user's interests as sources of evidence in order to tune the accuracy of documents returned in response to the user query. The model's fundation comes from influence diagrams which are extension of Bayesian graphs, dedicated to decision-making problems. Hence, query evaluation is carried out as an inference process that aims to computing an aggregated utility of a document by considering its relevance to the query but also the corresponding utility with regard to the user's topics of interest. Experimental results using enhanced TREC collections indicate that our personalized retrieval model is effective.

Mots clés: Information retrieval; Bayesian methods; Decision making; Search engines; Query processing; Multitasking; Data mining; Clustering algorithms; Context modeling; Feedback

Domaines: Recherche d'information [cs.IR]

Lynda Tamine, Mohand Boughanem, Nesrine Zemirli. Exploiting Multi-Evidence from Multiple User's Interests to Personalizing Information Retrieval. 2nd International Conference on Digital Information Management (ICDIM 2007), IEEE, Oct 2007, Lyon, France. pp.7-12, ⟨10.1109/ICDIM.2007.4444192⟩. ⟨hal-00359554⟩