Auswahl der wissenschaftlichen Literatur zum Thema „Recommandation d'articles“
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Zeitschriftenartikel zum Thema "Recommandation d'articles"
Haughian, Richard M. „The Role of the Church in Aging: Implications for Policy and Action. Michael C. Hendrickson (Ed.). New York: The Haworth Press, 1985, pp. 178. (Hard: $29.95 U.S., Soft: $19.95 U.S.) (Text soft price; 5 or more copies only: $9.95). - The Role of the Church in Aging, Volume II: Implications for Practice and Service. Michael C. Hendrickson (Ed.). New York: The Haworth Press, 1985, pp. 105. (Hard: $18.95 U.S.) (Text soft price; 5 or more copies only: $19.95)“. Canadian Journal on Aging / La Revue canadienne du vieillissement 6, Nr. 4 (1987): 318–24. http://dx.doi.org/10.1017/s0714980800007595.
Der volle Inhalt der QuelleJoy, Melanie S., Gary R. Matzke, Deborah K. Armstrong, Michael A. Marx und Barbara J. Zarowitz. „A Primer on Continuous Renal Replacement Therapy for Critically Ill Patients“. Annals of Pharmacotherapy 32, Nr. 3 (März 1998): 362–75. http://dx.doi.org/10.1345/aph.17105.
Der volle Inhalt der QuelleKennie, Natalie R., Brenda G. Schuster und Thomas R. Einarson. „Critical Analysis of the Pharmaceutical Care Research Literature“. Annals of Pharmacotherapy 32, Nr. 1 (Januar 1998): 17–26. http://dx.doi.org/10.1177/106002809803200101.
Der volle Inhalt der QuelleMorin, Katell Hernandez, und Franck Barbin. „Le projet OPTIMICE : une optimisation de la qualité des traductions de métadonnées par la collaboration entre acteurs du monde scientifique et traduction automatique“. Journal of Data Mining & Digital Humanities Towards robotic translation?, III. Biotranslation vs.... (10.01.2023). http://dx.doi.org/10.46298/jdmdh.9117.
Der volle Inhalt der QuelleDissertationen zum Thema "Recommandation d'articles"
You, Wei. „Un système à l'approche basée sur le contenu pour la recommandation personnalisée d'articles de recherche“. Compiègne, 2011. http://www.theses.fr/2011COMP1922.
Der volle Inhalt der QuellePersonalized research paper recommendation filters the publications according to the specific research interests of users, which could significantly alleviate the information overload problem. Content-based filtering is a promising solution for this task because it can effectively exploit the textual-nature of research papers. A content-based recommender system usually concerns three essential issues: item representation, user profiling, and a model that provides recommendations by comparing candidate item's content representation with the target user's interest representation. In this dissertation, we first propose an automatic keyphrase extraction technique for scientific documents, which improves the existing approaches by using a more precise location for potential keyphrases and a new strategy for eliminating the overlap in the output list. This technique helps to implement the representation of candidate papers and the analysis of users' history papers. Then for modeling the users' information needs, we present a new ontology-based approach. The basic idea is that each user is represented as an instance of a domain ontology in which concepts are assigned interest scores reflecting users' degree of interest. We distinguish senior researchers and junior researchers by deriving their individual research interests from different history paper sets. We also takes advantage of the structure of the ontology and apply spreading activation model to reason about the interests of users. Finally, we explore a novel model to generate recommendations by resorting to the Dempster-Shafer theory. Keyphrases extracted from the candidate paper are considered as sources of evidence. Each of them are linked to different levels of user interest and the strength of each link is quantified. Different from other similarity measures between user profiles and candidate papers, our recommendation result produced by combining all evidence is able to directly indicate the possibility that a user might be interested in the candidate paper. Experimental results show that the system we developed for personalized research paper recommendation outperforms other state-of-the-art approaches. The proposed method can be used as a generic way for addressing different types of recommendation problems
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.
Der volle Inhalt der QuelleEffective 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
Hay, Julien. „Apprentissage de la représentation du style écrit, application à la recommandation d’articles d’actualité“. Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG010.
Der volle Inhalt der QuelleUser modeling is an essential step when it comes to recommending products and offering services automatically. Social networks are a rich and abundant resource of user data (e.g. shared links, posted messages) that allow to model their interests and preferences. In this thesis, we propose to exploit news articles shared on social networks in order to enrich existing models with a new textual feature: the writing style. This thesis, at the intersection of the fields of natural language processing and recommender systems, focuses on the representation learning of writing style and its application to news recommendation. As a first step, we propose a new representation learning method that aims to project any document into a reference stylometric space. The hypothesis being tested is that such a space can be generalized by a sufficiently large set of reference authors, and that the vector projections of the writings of a "new" author will be stylistically close to the writings of a consistent subset of these reference authors. In a second step, we propose to exploit the stylometric representation for news recommendation by combining it with other representations (e.g. topical, lexical, semantic). We seek to identify the most relevant and complementary characteristics that can allow a more relevant and better quality recommendation of articles. The hypothesis that motivated this work is that the reading choices of individuals are not only influenced by the content (e.g. the theme of news articles, the entities mentioned), but also by the form (i.e. the style that can, for example, be descriptive, satirical, composed of personal anecdotes, interviews). The experiments conducted show that not only does writing style play a role in individuals' reading preferences, but also that, when combined with other textual features, it increases the accuracy and quality of recommendations in terms of diversity, novelty and serendipity