Exploration of User Groups in VEXUS

We introduce VEXUS, an interactive visualization framework for exploring user data to fulfill tasks such as finding a set of experts, forming discussion groups and analyzing collective behaviors. User data is characterized by a combination of demographics like age and occupation, and actions such as rating a movie, writing a paper, following a medical treatment or buying groceries. The ubiquity of user data requires tools that help explorers, be they specialists or novice users, acquire new insights. VEXUS lets explorers interact with user data via visual primitives and builds an exploration profile to recommend the next exploration steps. VEXUS combines state-of-the-art visualization techniques with appropriate indexing of user data to provide fast and relevant exploration

Changing the focus: worker-centric optimization in human-in-the-loop computations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back to humans, and study different data analytics problems, by recognizing characteristics of the human workers, and how to incorporate those in a principled fashion inside the computation loop. The first contribution of this dissertation is to propose an optimization framework and a real world system to personalize worker’s behavior by developing a worker model and using that to better understand and estimate task completion time. The framework judiciously frames questions and solicits worker feedback on those to update the worker model. Next, improving workers skills through peer interaction during collaborative task completion is studied. A suite of optimization problems are identified in that context considering collaborativeness between the members as it plays a major role in peer learning. Finally, “diversified” sequence of work sessions for human workers is designed to improve worker satisfaction and engagement while completing tasks

Optimization-based User Group Management : Discovery, Analysis, Recommendation

User 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)

WELLNESS PROFILING ON SOCIAL NETWORKS

Ph.DDOCTOR OF PHILOSOPH

Interactive User Group Analysis

International audienceUser 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, ananalyst 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

Interactive User Group Analysis Technical Report

Les 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