1,721,064 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recommandation basée sur les intérêts utilisateurs pour les systèmes d'informatique décisionnelle modernes
No full textStocker de grandes quantités de données complexifie les interactions avec les systèmes de Business Intelligence (BI). Les systèmes de recommandation semblent un choix logique pour aider les utilisateurs dans leur analyse. Ils extraient des comportements de données historiques et suggèrent des actions personnalisées, potentiellement redondantes, via des scores de similarité. La diversité est essentielle pour améliorer la satisfaction des utilisateurs, d’où l’intérêt particulier accordé aux recommandations complémentaires. Nous avons étudié deux problèmes concrets d’exploration de données en BI et proposons de découvrir et exploiter les intentions utilisateur pour fournir deux recommandeurs de requête. Le premier, un recommandeur collaboratif réactif original basé sur l’intention, recommande des séquences de requêtes à l’utilisateur pour poursuivre son analyse. Le second propose proactivement un ensemble de requêtes pour compléter un rapport BI, en fonction di contexte utilisateur.The storage of big amounts of data may lead to a series of long questions towards the expected solution which complicates user interactions with Business Intelligence (BI) systems. Recommender systems appear as a natural solution to help the users complete their analysis. They try to discover user behaviors from the past logs and to suggest personalized actions by predicting lists of likeness scores, which may lead to redundant recommendations. Nowadays, diversity is becoming essential to improve users’ satisfaction, thus, a special interest is dedicated to complementary recommendation. We studied two concrete data exploration problems in BI and we propose to discover and leverage the user intents to provide two query recommenders. The first, an original reactive collaborative Intent-based Recommender, recommends sequences of queries for the user to pursue her analysis. The second one proactively proposes a bundle of queries to complete user BI report, based on the user intents
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Multi-Objective optimization for the construction and personalization of ad campaigns
No full textL'objectif de cette thèse est d'étudier et de développer de nouvelles approches d'optimisation multi-objectifs et d'intelligence artificielle pour l'allocation et la personnalisation de la planification des campagnes publicitaires multi-marques. Le problème d'allocation des campagnes publicitaires multimarques est un problème d'optimisation combinatoire multi-objectif NP-difficile qui consiste, étant donné un ensemble de campagnes publicitaires d'annonceurs et un ensemble d'écrans commerciaux avec du temps de diffusion (slots), à déterminer comment les spots publicitaires devraient être alloués à un sous-ensemble d'écrans afin de maximiser le revenu total du réseau de télévision, de respecter les exigences des annonceurs et les restrictions de l'inventaire publicitaire limité. A cette fin, dans cette thèse, nous étendons l'état de l'art en : (i) fournissant une formalisation multi-acteurs et multi-objectifs pour le problème en question, dans lequel, les exigences de toutes les parties prenantes dans le processus d'allocation de la publicité sont modélisées et prises en considération, (ii) proposant un nouvel algorithme évolutionnaire multi-objectif qui incorpore les préférences des décideurs et pourrait être adapté à des fronts de Pareto de formes différentes, (iii) suggérant un nouveau mécanisme de promotion de la diversité pour améliorer la diversité des solutions proposées, (iv) proposer un nouveau cadre basé sur l'apprentissage par transfert pour apprendre la représentation des caractéristiques des régions optimales de Pareto du problème source, afin de l'exploiter comme une heuristique pour la résolution et la personnalisation de problèmes cibles similaires, (v) proposer un générateur de données synthétiques pour évaluer et améliorer l'évolutivité des allocateurs de plans médias suggérés.The objective of this thesis is to study and develop new Multi-objective optimization and artificial intelligence approaches for the allocation and personalization of multi-brand advertising campaigns scheduling. The multi-brand advertising campaign allocation problem is an NP-Hard, Many-Objective combinatorial optimization problem that consists of, given a set of advertiser campaigns and a set of commercial breaks with airtime (slots), determining how campaign spots (brand messages) should be allocated to a subset of breaks in order to maximize total TV network revenue and evenness with respect to advertisers' requirements and limited advertising inventory restrictions. To this end, in this thesis, we extend the state of the art by: (i) providing a Multi-Stakeholder, Many-objective formalization for the problem at hand, in which the requirements of all stakeholders in the advertisement allocation process is modeled and taken into consideration, (ii) proposing new Many-objective evolutionary algorithm that incorporates decision makers' preferences and could be adapted to differently shaped pareto fronts, (iii) suggesting a new diversity promotion mechanism to improve the diversity of proposed solutions, (iv) proposing a new transfer learning based framework for learning the feature representation of the Pareto optimal regions of the source problem and exploiting it as a heuristic for the resolution and personalization of similar target problems, (v) proposing a synthetic data generator to evaluate and improve the scalability of suggested media plan allocators
Automatic Web Pages Author Extraction
No full textInternational audienceThis paper addresses the problem of automatically extracting the author from heterogeneous HTML resources as a sub problem of automatic metadata extraction from (Web) documents. We take a supervised machine learning approach to address the problem using a C4.5 Decision Tree algorithm. The particularity of our approach is that it focuses on both, structure and contextual information. A semi-automatic approach was conducted for corpus expansion in order to help annotating the dataset with less human effort. This paper shows that our method can achieve good results (more than 80% in term of F1-measure) despite the heterogeneity of our corpus
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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