1,720,985 research outputs found
Optimization-based User Group Management : Discovery, Analysis, Recommendation
Get PDFUser data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. User data is a special type of data that is described by user demographics (e.g., age, gender, occupation, etc.) and user activities (e.g., rating, voting, watching a movie, etc.) The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large-scale data analytics. However, analysis tools for user data is still lacking.In this thesis, we believe there exists a unique opportunity to analyze user data in the form of user groups. This is in contrast with individual user analysis and also statistical analysis on the whole population. A group is defined as set of users whose members have either common demographics or common activities. Group-level analysis reduces the amount of sparsity and noise in data and leads to new insights. In this thesis, we propose a user group management framework consisting of following components: user group discovery, analysis and recommendation.The very first step in our framework is group discovery, i.e., given raw user data, obtain user groups by optimizing one or more quality dimensions. The second component (i.e., analysis) is necessary to tackle the problem of information overload: the output of a user group discovery step often contains millions of user groups. It is a tedious task for an analyst to skim over all produced groups. Thus we need analysis tools to provide valuable insights in this huge space of user groups. The final question in the framework is how to use the found groups. In this thesis, we investigate one of these applications, i.e., user group recommendation, by considering affinities between group members.All our contributions of the proposed framework are evaluated using an extensive set of experiments both for quality and performance.Les donn ́ees utilisateurs sont devenue de plus en plus disponibles dans plusieurs do- maines tels que les traces d'usage des smartphones et le Web social. Les donn ́ees util- isateurs, sont un type particulier de donn ́ees qui sont d ́ecrites par des informations socio-d ́emographiques (ex., ˆage, sexe, m ́etier, etc.) et leurs activit ́es (ex., donner un avis sur un restaurant, voter, critiquer un film, etc.). L'analyse des donn ́ees utilisa- teurs int ́eresse beaucoup les scientifiques qui travaillent sur les ́etudes de la population, le marketing en-ligne, les recommandations et l'analyse des donn ́ees `a grande ́echelle. Cependant, les outils d'analyse des donn ́ees utilisateurs sont encore tr`es limit ́es.Dans cette th`ese, nous exploitons cette opportunit ́e et proposons d'analyser les donn ́ees utilisateurs en formant des groupes d'utilisateurs. Cela diff`ere de l'analyse des util- isateurs individuels et aussi des analyses statistiques sur une population enti`ere. Un groupe utilisateur est d ́efini par un ensemble des utilisateurs dont les membres parta- gent des donn ́ees socio-d ́emographiques et ont des activit ́es en commun. L'analyse au niveau d'un groupe a pour objectif de mieux g ́erer les donn ́ees creuses et le bruit dans les donn ́ees. Dans cette th`ese, nous proposons un cadre de gestion de groupes d'utilisateurs qui contient les composantes suivantes: d ́ecouverte de groupes, analyse de groupes, et recommandation aux groupes.La premi`ere composante concerne la d ́ecouverte des groupes d'utilisateurs, c.- `a-d., compte tenu des donn ́ees utilisateurs brutes, obtenir les groupes d'utilisateurs en op- timisantuneouplusieursdimensionsdequalit ́e. Ledeuxi`emecomposant(c.-`a-d., l'analyse) est n ́ecessaire pour aborder le probl`eme de la surcharge de l'information: le r ́esultat d'une ́etape d ́ecouverte des groupes d'utilisateurs peut contenir des millions de groupes. C'est une tache fastidieuse pour un analyste `a ́ecumer tous les groupes trouv ́es. Nous proposons une approche interactive pour faciliter cette analyse. La question finale est comment utiliser les groupes trouv ́es. Dans cette th`ese, nous ́etudions une applica- tion particuli`ere qui est la recommandation aux groupes d'utilisateurs, en consid ́erant les affinit ́es entre les membres du groupe et son ́evolution dans le temps.Toutes nos contributions sont ́evalu ́ees au travers d'un grand nombre d'exp ́erimentations `a la fois pour tester la qualit ́e et la performance (le temps de r ́eponse)
User Group Analytics Survey and Research Opportunities
No full textInternational audienceUser data can be acquired from various domains and is characterized by a combination of demographics such as age and occupation, and user actions such as rating a movie or recording one's blood pressure. User data is appealing to analysts in their role as data scientists who seek to conduct large-scale population studies, and gain insights on various population segments. It is also appealing to users in their role as information consumers who use the social Web for routine tasks such as finding a book club or choosing a physical activity. User data analytics usually relies on identifying group-level behaviors such as "Asian women who publish regularly in databases". Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this survey, we discuss different approaches for each component of user group analytics, i.e., discovery, exploration, and visualization. We focus on related work which arises from combining those components. We also discuss challenges and future directions of having an all-in-one system, where all those components are combined. This survey has been presented in the form of two tutorials [1], [2]
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“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
Guided exploration of user groups
No full textInternational audienceFinding a set of users of interest serves several applications in behavioral analytics. Often times, identifying users requires to explore the data and gradually choose potential targets. This is a special case of Exploratory Data Analysis (EDA), an iterative and tedious process. In this paper, we formalize and solve the problem of guided exploration of user groups whose purpose is to find target users. We model exploration as an iterative decision-making process, where an agent is shown a set of groups, chooses users from those groups, and selects the best action to move to the next step. To solve our problem, we apply reinforcement learning to discover an efficient exploration strategy from a simulated agent experience, and propose to use the learned strategy to recommend an exploration policy that can be applied to the same task for any dataset. Our framework accepts a wide class of exploration actions and does not need to gather exploration logs. Our experiments show that the agent naturally captures manual exploration by human analysts , and succeeds to learn an interpretable and transferable exploration policy
Eighth Workshop on Human-In-the-Loop Data Analytics (HILDA)
No full textInternational audienceHILDA brings together researchers and practitioners to exchange ideas and results on human-data interaction. It explores how data management and analysis can be made more effective when taking into account the people who design and build these processes as well as those who are impacted by their results. Following the past two years, we plan to continue to focus on this year's workshop on early-stage research that is promising and exciting, which includes pairing each accepted paper with a mentor. The theme for this edition of the workshop is HILDA and Large Language Models. However, the workshop is not limited to this theme and other topics are also of interest. In this summary, we describe the workshop, its main focus areas, our review and mentorship plan as well as the keynote and panel sessions
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
Interactive User Group Analysis Technical Report
No full textLes rapports de recherche du LIG - ISSN : 2105-0422User data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. The analysis of user data is appealing to scientists who work on population studies, recommendations, and large-scale data analytics. We argue for the need for an interactive analysis to understand the multiple facets of user data and address different analytics scenarios. Since user data is often sparse and noisy, we propose to produce labeled groups that describe users with common properties and develop IUGA, an interactive framework based on group discovery primitives to explore the user space. At each step of IUGA, an analyst visualizes group members and may take an action on the group (add/remove members) and choose an operation (exploit/explore) to discover more groups and hence more users. Each discovery operation results in k most relevant and diverse groups. We formulate group exploitation and exploration as optimization problems and devise greedy algorithms to enable efficient group discovery. Finally, we design a principled validation methodology and run extensive experiments that validate the effectiveness of IUGA on large datasets for different user space analysis scenarios
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