Social Priors to Estimate Relevance of a Resource

Ismail Badache (1, 2) , Mohand Boughanem (3, 4)

1 R2I - Recherche d’information et Interactions
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
3 IRIT-SIG - Systèmes d’Informations Généralisées
4 UT3 - Université Toulouse III - Paul Sabatier

In this paper we propose an approach that exploits social data associated with a Web resource to measure its a priori relevance. We show how these interaction traces left by the users on the resources, which are in the form of social signals as the number of like and share, can be exploited to quantify social properties such as popularity and reputation. We propose to model these properties as a priori probability that we integrate into language model. We evaluated the effectiveness of our approach on IMDb dataset containing 167438 resources and their social signals collected from several social networks. Our experimental results are statistically significant and show the interest of integrating social properties in a search model to enhance the information retrieval.

Mots clés: Information retrieval; Social signals; Priors; Language models; Feature evaluation

Domaines: Recherche d'information [cs.IR]

Ismail Badache, Mohand Boughanem. Social Priors to Estimate Relevance of a Resource. the 5th Information Interaction in Context Symposium, Aug 2014, Regensburg, Germany. pp.106-114, ⟨10.1145/2637002.2637016⟩. ⟨hal-02378996⟩