Literatura científica selecionada sobre o tema "Recommandation des actualités"
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Artigos de revistas sobre o assunto "Recommandation des actualités"
Auxéméry, Yann. "Actualités des recommandations en psychotraumatologie". Annales Médico-psychologiques, revue psychiatrique 174, n.º 4 (maio de 2016): 304–8. http://dx.doi.org/10.1016/j.amp.2015.11.007.
Texto completo da fonteGeorges, M. "Réadaptation respiratoire : actualités et futures recommandations". Revue des Maladies Respiratoires Actualités 15, n.º 1 (setembro de 2023): 1S114–1S124. http://dx.doi.org/10.1016/s1877-1203(23)00083-6.
Texto completo da fonteEmile, Carole. "Helicobacter pylori : actualités et nouvelles recommandations thérapeutiques". Option/Bio 27, n.º 549-550 (setembro de 2016): 21–22. http://dx.doi.org/10.1016/s0992-5945(16)30233-1.
Texto completo da fonteTandonnet, L. "Manifestations dépressives à l’adolescence : actualités et recommandations". European Psychiatry 29, S3 (novembro de 2014): 613. http://dx.doi.org/10.1016/j.eurpsy.2014.09.234.
Texto completo da fonteAmstutz, Vincent, Bernard Favrat, Jacques Cornuz e Marc-Antoine Krieg. "[b]Vitamine D[/b] : actualité et recommandations". Revue Médicale Suisse 7, n.º 319 (2011): 2332–38. http://dx.doi.org/10.53738/revmed.2011.7.319.2332.
Texto completo da fonteHamel de Monchenault, Gautier, e Laurent Vacavant. "La stratégie européenne en physique des particules". Reflets de la physique, n.º 68 (março de 2021): 24–27. http://dx.doi.org/10.1051/refdp/202168024.
Texto completo da fonteLetonturier, Philippe. "Des recommandations actualisées sur la chirurgie de l’obésité". La Presse Médicale 33, n.º 2 (janeiro de 2004): 141. http://dx.doi.org/10.1016/s0755-4982(04)98506-5.
Texto completo da fonteCornu, J. N., M. Rouprêt, H. Lang, J. A. Long, Y. Neuzillet, J. J. Patard, T. Piéchaud et al. "Prise en charge du cancer du rein en 2007 : actualités et recommandations". Progrès en Urologie 18 (julho de 2008): S81—S87. http://dx.doi.org/10.1016/s1166-7087(08)73667-4.
Texto completo da fonteDemoly, P. "Entre recommandations et pratique, quelles sont les actualités de la rhinite allergique ?" Revue Française d'Allergologie 49 (dezembro de 2009): S61—S64. http://dx.doi.org/10.1016/s1877-0320(09)73412-8.
Texto completo da fonteSiegrist, Claire-Anne. "Actualités vaccinologiques 2005: nouvelle catégorie de recommandations pour une protection vaccinale optimale". Revue Médicale Suisse 2, n.º 47 (2006): 67–70. http://dx.doi.org/10.53738/revmed.2006.2.47.0067.
Texto completo da fonteTeses / dissertações sobre o assunto "Recommandation des actualités"
Werner, David. "Indexation et recommandation d'informations : vers une qualification précise des items par une approche ontologique, fondée sur une modélisation métier du domaine : application à la recommandation d'articles économiques". Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS078/document.
Texto completo da fonteEffective management of large amounts of information has become a challenge increasinglyimportant for information systems. Everyday, new information sources emerge on the web. Someonecan easily find what he wants if (s)he seeks an article, a video or a specific artist. However,it becomes quite difficult, even impossible, to have an exploratory approach to discover newcontent. Recommender systems are software tools that aim to assist humans to deal withinformation overload. The work presented in this Phd thesis proposes an architecture for efficientrecommendation of news. In this document, we propose an architecture for efficient recommendationof news articles. Our ontological approach relies on a model for precise characterization of itemsbased on a controlled vocabulary. The ontology contains a formal vocabulary modeling a view on thedomain knowledge. Carried out in collaboration with the company Actualis SARL, this work has ledto the marketing of a new highly competitive product, FristECO Pro’fil
Feng, Yuting. "Diffusion-Aware Recommendation in Social Media". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG009.
Texto completo da fonteWith the increasing popularity of social media as pathways to information, making recommendations in specific social scenarios deserves attention, where the information diffusion patterns and influence mechanisms therein are exploited. We strive in our work to develop models and algorithms for serving information to users in social media, either in a direct user-based (personalized) way or in an indirect audience-based way, with the former pertaining to news recommendation and the latter referring to fairness in influence maximization. News recommendation systems are generally based on the semantic content of news items and user profiles, whereas the underlying recommendation scenario is ignored. We consider in our PhD work a diffusion and influence-aware perspective on the news recommendation problem, and we first propose a lightweight deep learning approach for it, called DSN. This approach targets news recommendation in micro-blogging platforms, such as Twitter or Weibo, whose extreme data velocity demands a satisfactory trade-off between the model's complexity and its effectiveness. We use graph embeddings -- node representations that are indicative of news diffusion patterns -- leading to valuable social-related information for recommendations. To merge the semantics and social-related representations of news, a specially designed convolutional neural network for joint feature representation (SCNN) is used as the news encoder, while an attention model automatically aggregates the different interests of users. To further exploit the time dimension, with a sequential recommendation perspective on news recommendation in the micro-blogging scenario, we propose secondly in our PhD work an alternative deep-learning based recommendation model, which is also diffusion and influence-aware, called Influence-Graph News Recommender (IGNteR). It is a content-based deep recommendation model that jointly exploits all the data facets that may impact adoption decisions, namely semantics, diffusion-related features pertaining to local and global influence among users, temporal attractiveness, and timeliness, as well as dynamic user preferences. We perform extensive experiments on the same real-world datasets, showing that IGNiteR outperforms the state-of-the-art deep-learning based news recommendation methods.For the indirect and audience-based recommendation setting, we focus on influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while ensuring that selected sensitive user attributes (e.g., gender location, origin, race, etc.) are fairly affected, i.e., are proportionally similar between the original network and the affected users. We propose two data-driven approaches: (a) fairness-based participant sampling (FPS) and (b) fairness as context (FAC), which are based on learning node representations (embeddings) to extract spread-related user features from diffusion cascades information, instead of the social connectivity, and in this way we can deal with very large graphs. The extracted features are then used in selecting influencers that maximize the influence spread, while also being fair with respect to the chosen sensitive attributes. In FPS, fairness and cascade length information are considered independently in the decision-making process, while FAC considers these information facets jointly and takes into account correlations between them. The proposed algorithms are generic and represent the first policy-driven solutions that can be applied to arbitrary sets of sensitive attributes at scale
Capítulos de livros sobre o assunto "Recommandation des actualités"
Pélissier, Jacques. "Pratiques professionnelles et recommandations: orientation des patients atteints d’AVC". In Accident vasculaire cérébral et médecine physique et de réadaptation: Actualités en 2010, 7. Paris: Springer Paris, 2010. http://dx.doi.org/10.1007/978-2-8178-0109-4_2.
Texto completo da fonte