48 research outputs found
Enrichissement de requĂȘtes pour la recherche documentaire selon une classification non supervisĂ©e
National audienceUne difficultĂ© majeure dans l'utilisation d'un systĂšme de recherche documentaire est le choix du vocabulairĂš a employer pour exprimer une requĂȘte. L'enrichisse-ment de la requĂȘte peut prendre plusieurs formes : ajout de mots extraits automatiquement des documents rapportĂ©s, rĂ©estimation des poids attribuĂ©sĂ chacun des mots de la requĂȘte initiale, etc. Le systĂšme de re-cherche documentaire SIAC est utilisĂ© pour extraire un premier jeu de documentsĂ partir d'une requĂȘte. Une mĂ©thode de classification non supervisĂ©e,Ă base d'arbres de dĂ©cision, est ensuite exploitĂ©e pour clas-ser les phrases des documents trouvĂ©s selon qu'elles contiennent ou non certains mots extraits automa-tiquement de l'ensemble des documents rapportĂ©s.Ă chaque noeud de l'arbre, peutĂȘtre associĂ©e une expression boolĂ©enne mettant en jeu les mots sĂ©lectionnĂ©s lors de la classification. Nous montrons,Ă l'aide des donnĂ©es de la seconde campagne d'Ă©valuation Amaryl-lis, que la rĂ©Ă©criture de la requĂȘte suivant les expressions boolĂ©ennes correspondant aux meilleures feuilles permet d'amĂ©liorer la prĂ©cision de la recherche docu-mentaire. Mots Clef Recherche documentaire, enrichissement de requĂȘte, classification automatique, arbres de dĂ©cision non su-pervisĂ©s. 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
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