A scalable mining of frequent quadratic concepts in d-folksonomies

Folksonomy mining is grasping the interest of web 2.0 community since it represents the core data of social resource sharing systems. However, a scrutiny of the related works interested in mining folksonomies unveils that the time stamp dimension has not been considered. For example, the wealthy number of works dedicated to mining tri-concepts from folksonomies did not take into account time dimension. In this paper, we will consider a folksonomy commonly composed of triples and we shall consider the time as a new dimension. We motivate our approach by highlighting the battery of potential applications. Then, we present the foundations for mining quadri-concepts, provide a formal definition of the problem and introduce a new efficient algorithm, called QUADRICONS for its solution to allow for mining folksonomies in time, i.e., d-folksonomies. We also introduce a new closure operator that splits the induced search space into equivalence classes whose smallest elements are the quadri-minimal generators. Carried out experiments on large-scale real-world datasets highlight good performances of our algorithm

Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules

IRIT at TREC 2014 Contextual Suggestion Track

International audienceIn this work, we give an overview of our participation in the TREC 2014 Contextual Suggestion Track. To address the retrieval of attraction places, we propose a fuzzy-based document combination approach for preference learning and context processing.We use the open web in our submission and make use of both criteria users preferences and geographical location criteria

Prise en compte des préférences des utilisateurs pour l'estimation de la pertinence multidimensionnelle d'un document

National audienceDans ce papier, nous proposons une nouvelle approche d’agrégation personnalisée pour l’estimation de la pertinence multidimensionnelle. L’approche est basée sur un opérateur d’agrégation mathématique qui utilise une mesure floue permettant la quantification de l’importance estimée des critères pour chaque utilisateur ainsi que leur degré d’interactivité ou d’interdépendance. Nous évaluons l’opérateur d’agrégation proposé en utilisant la collection de test standard fournie avec par la tâche “Contextual Suggestion” de TREC 2013. Les résultats expérimentaux obtenus montrent l’impact de la personnalisation sur les performances de recherche

L'intégrale de Choquet discrète pour l'agrégation de pertinence multidimensionnelle

International audienceDans ce papier, nous nous intéressons à étudier le problème de l'agrégation multicritères dans le domaine de la recherche d'information (RI). Nous proposons une nouvelle approche basée sur l'intégrale de Choquet pour l'agrégation de pertinence multidimensionnelle. La principale originalité de cet opérateur, outre sa capacité à modéliser des interactions entre les différentes dimensions de pertinence, est sa capacité à généraliser de nombreuses fonctions d'agrégation classiques. L'évaluation de l'efficacité de notre approche est effectuée dans une tâche de recherche de tweets, où les critères conjointement utilisés sont, la pertinence thématique, l'autorité et la fraîcheur. Les résultats expérimentaux obtenus sur la collection de test fournie par la tâche Microblog de TREC 2011 montrent la pertinence de notre proposition