Commune de Saint-Génard

Identifiant de l'opération archéologique : 204490 Date de l'opération : 2009 (PI) Une prospection pédestre sur le territoire de la commune de Saint-Génard (79) a permis de repérer en surplomb de vallée deux sites gallo-romains distants de 3 km, le long de 2 rivières, la Marseillaise et la Berlande, qui traversent la commune. Le premier, près de la source de la Marseillaise, étaye les textes faisant état d'une implantation de cette époque (villa); du mobilier céramique (commune grise) a été tr..

Skipping-Based Collaborative Recommendations inspired from Statistical Language Modeling

Due to the almost unlimited resource space on the Web, efficient search engines and recommender systems have become a key element for users to find resources corresponding to their needs. Recommender systems aims at helping users in this task by providing them some pertinent resources according to their context and their profiles, by applying various techniques such as statistical and knowledge discovery algorithms. One of the most successful approaches is Collaborative Filtering, which consists in considering user ratings to provide recommendations, without considering the content of the resources; however the ratings are the only criterion taken into account to provide the recommendations, although including some other criterion should enhance their accuracy. One such criterion is the context, which can be geographical, meteorological, social, etc. In this chapter we focus on the temporal context, more specifically on the order in which the resources were consulted. The appropriateness of considering the order is domain dependent: for instance, it seems of little help in domains such as online moviestores, in which user transactions are barely sequential; however it is especially appropriate for domains such as Web navigation, which has a sequential structure. We propose to follow this direction for this domain, the challenge being to find a low enough complexity sequential model while providing a better accuracy. We first put forward similarities between Web navigation and natural language, and propose to adapt statistical language models to Web navigation to compute recommendations. Second, we propose a new model inspired from the n-gram skipping model. This model has several advantages: (1) It has both a low time and a low space complexity while providing a full coverage, (2) it is able to handle parallel navigations and noise, (3) it is able to perform recommendations in an anytime framework, (4) weighting schemes are used to alleviate the importance of distant resources. Third, we provide a comparison of this SLM inspired model to the state of the art in terms of features, complexity, accuracy and robustness and present experimental results. Tests are performed on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous resources highly improves the accuracy, and that the anytime configuration is able to provide a satisfying trade-off between an even lower computation time and a good accuracy while conserving a good coverage

An exploration of evidence-based policy in Ireland: health and social inclusion

Ireland is a small country with a history of a social partnership approach to policy making. This paper considers how the ambition of government to utilise an evidence-based approach to policy making plays out against this partnership agenda. Drawing on the authors\u27 experiences and personal reflections, the paper considers how these issues operate within a number of health and social inclusion policy areas, and it explores the role of stakeholders\u27 expectations and involvement in generating evidence for policy

The Effect of Industrialization on Children�s Education. The Experience of Mexico

We use census data to examine the impact of industrialization on children’s education in Mexico. We find no evidence of reverse causality in this case.  We find small positive effects of industrialization on primary education, effects which are larger for domestic manufacturing than for export-intensive assembly (maquiladoras). In contrast, teen-aged girls in Mexican counties (municipios) with more growth in maquiladora employment 1990-2000 have significantly less educational attainment than do girls in low-growth counties. These results shed light on literatures analyzing the impacts of industrialization, foreign investment, and intra-household bargaining powe

Natural language processing for usage based indexing of web resources

The original publication is available at www.springerlink.com ISBN: 978-3-540-71494-1; ISSN 0302-9743 (Print) 1611-3349 (Online)International audienceThe identification of reliable and interesting items on Internet becomes more and more difficult and time consuming. This paper is a position paper describing our intended work in the framework of multimedia information retrieval by browsing techniques within web navigation. It relies on a usage-based indexing of resources: we ignore the nature, the content and the structure of resources. We describe a new approach taking advantage of the similarity between statistical modeling of language and document retrieval systems. A syntax of usage is computed that designs a Statistical Grammar of Usage (SGU). A SGU enables resources classification to perform a personalized navigation assistant tool. It relies both on collaborative filtering to compute virtual communities of users and classical statistical language models. The resulting SGU is a community dependent SGU

Usage based indexing of web resources with natural language processing

International audienceDue to the huge amount of available information via Internet, the identification of reliable and interesting items becomes more and more difficult and time consuming. This paper is a position paper describing our intended work in the framework of multimedia information retrieval by browsing techniques within web navigation. It relies on a usage-based indexing of resources: we ignore the nature, the content and the structure of resources. We describe a new approach taking advantage of the similarity between statistical modeling of language and document retrieval systems. A syntax of usage is computed that designs a Statistical Grammar of Usage (SGU). A SGU enables resources classification to perform a personalized navigation assistant tool. It relies both on collaborative filtering to compute virtual communities of users and a new distance dependent trigger model. The resulting SGU is a community dependent SGU

Inspiration des sondages d'opinion pour réduire la latence en filtrage collaboratif.

International audienceCollaborative filtering is one of the most popular approaches in recommender systems. In collaborative filtering, the system exploits the ratings of the active user's like-minded users: his neighbors, to estimate his ratings on the items he has not rated yet and recommend him the items with the highest estimated ratings. Collaborative filtering faces a latency problem: a new item cannot be recommended to any user while this item has not been rated a sufficiently high number of times. To alleviate this latency problem, we propose a new approach based on mentors, inspired from opinion polls. A mentor is a reliable, representative and trusted user. The knowledge of the ratings of the mentors on new items allows to estimate the ratings of the whole population. We show that, on the corpus tested, when using some of the mentor selection methods, only 6 mentors are sufficient, to reliably estimate the ratings of the whole population.Le filtrage collaboratif est l'une des approches les plus populaires des systèmes de recommandation. En filtrage collaboratif, le système cherche à estimer les préférences de l'utilisateur actif en exploitant les préférences (les notes) des utilisateurs similaires à cet utilisateur actif : ses voisins. Le filtrage collaboratif fait face au problème de latence : il ne peut recommander un nouvel item à des utilisateurs tant que cet item n'a pas été noté un nombre suffisant de fois. Pour diminuer ce problème de latence, nous proposons une approche à base de mentors s'inspirant des sondages d'opinion. Un mentor est un utilisateur fiable et représentatif, sur qui l'on peut compter. La connaissance des notes (opinions) des mentors sur les nouveaux items permet d'estimer les notes de la population entière. Nous montrons que, sur le corpus que nous avons utilisé, certaines méthodes de sélection des mentors requièrent les notes de seulement 6 mentors pour estimer les notes de la population entière, avec une erreur faible. Ainsi, lorsqu'un nouvel item est intégré dans le système, seules 6 notes sont requises pour faire des recommandations de bonne qualité à l'ensemble de la population