Flash reactivity: adaptive models in recommender systems

International audienceRecommendation systems take advantage of products and users information in order to propose items to targeted consumers. Collaborative recommendation systems, content-based recommendation systems and a few hybrid systems have been developed. We propose a dynamic and adaptive framework to overcome the usual issues of nowadays systems. We present a method based on adaptation in time in order to provide recommendations in phase with the present instant. The system includes a dynamic adaptation to enhance the accuracy of rating predictions by applying a new similarity measure. We did several experiments on films data from Vodkaster, showing that systems incorporating dynamic adaptation improve significantly the quality of recommendations compared to static ones

Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums

This paper proposes a new method to provide personalized tour recommendation for museum visits. It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy. This project includes researchers from computer and social sciences. Some results are obtained with numerical experiments. They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour. This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.Comment: 8 pages, 4 figures; Proceedings of the 2014 Federated Conference on Computer Science and Information Systems pp. 439-44

Enrichissement de requêtes pour la recherche documentaire selon une classification non supervisée

National audienceUne difficulté majeure dans l'utilisation d'un système de recherche documentaire est le choix du vocabulairè a employer pour exprimer une requête. L'enrichisse-ment de la requête peut prendre plusieurs formes : ajout de mots extraits automatiquement des documents rapportés, réestimation des poids attribuésà chacun des mots de la requête initiale, etc. Le système de re-cherche documentaire SIAC est utilisé pour extraire un premier jeu de documentsà partir d'une requête. Une méthode de classification non supervisée,à base d'arbres de décision, est ensuite exploitée pour clas-ser les phrases des documents trouvés selon qu'elles contiennent ou non certains mots extraits automa-tiquement de l'ensemble des documents rapportés.À chaque noeud de l'arbre, peutêtre associée une expression booléenne mettant en jeu les mots sélectionnés lors de la classification. Nous montrons,à l'aide des données de la seconde campagne d'évaluation Amaryl-lis, que la réécriture de la requête suivant les expressions booléennes correspondant aux meilleures feuilles permet d'améliorer la précision de la recherche docu-mentaire. Mots Clef Recherche documentaire, enrichissement de requête, classification automatique, arbres de décision non su-pervisés. Abstract Natural language query formulation is a crucial task in the information retrieval (IR) process. Automatic expanding and refining of queries can be realized in different ways : extracting some words from top retrieved documents (retrieval feedback) or from thesauri, computing new query term weights according to top retrieved documents... In this paper, the information retrieval system SIAC is employed to obtain an initial set of documents from a query. Then, a classification method employing unsupervised decision trees (UDTs) is performed to classify the document retrieved sentences according to some words extracted automatically from these documents (some sentences contain the chosen words, some do not). A boolean expression composed of these selected words is directly associated to each decision tree node. This paper shows that expanding queries with the words connected with the best nodes allows to significantly improve retrieval precision