Search results for "User"
showing 10 items of 1060 documents
User profile matching in social networks
2010
International audience; Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) w…
Semantic User Profiling for Digital Advertising
2015
International audience; With the emergence of real-time distribution of online advertising space (“real-time bidding”), user profiling from web navigation traces becomes crucial. Indeed, it allows online advertisers to target customers without interfering with their activities. Current techniques apply traditional methods as statistics and machine learning, but suffer from their limitations. As an answer, the proposed approach aims to develop and evaluate a semantic-based user profiling system for digital advertising.
Customizing Semantic Profiling for Digital Advertising
2014
International audience; Personalization is the new magic buzzword of application development. To make the complexity of today's application functionalities and information spaces "digestible", customization has become the new go-to technique. But while those technologies aim to ease the consumption of media for their users, they suffer from the same problematic: in the age of Big Data, applications have to cope with a conundrum of heterogeneous information sources that have to be perceived, processed and interpreted. Researchers tend to aim for a maximum degree of integration to create the perfect, all-embracing personalization. The results are wide-range, but overly complex systems that su…
Ontology-based Integration of Web Navigation for Dynamic User Profiling
2015
The development of technology for handling information on a Big Data-scale is a buzzing topic of current research. Indeed, improved techniques for knowledge discovery are crucial for scientific and economic exploitation of large-scale raw data. In research collaboration with an industrial actor, we explore the applicability of ontology-based knowledge extraction and representation for today's biggest source of large-scale data, the Web. The goal is to develop a profiling application, based on the implicit information that every user leaves while navigating the online, with the goal to identify and model preferences and interests in a detailed user profile. This includes the identification o…
Automatic User Profile Mapping To Marketing Segments In A Big Data Context
2015
International audience; Within the discussion about the analysis methods for Big Data contexts, semantic technologies often get discarded for reasons of efficiency. While machine learning and statistics are known to have shortcomings when handling natural language, their advantages in terms of performance outweigh potential concerns. We argue that even when handling vast amounts of data, the usage of semantic technologies can be profitable and demonstrate this by developing an ontology-based system for automatically mapping user profiles to pre-defined marketing segments.
Adding Knowledge Extracted by Association Rules into Similarity Queries
2010
International audience; In this paper, we propose new techniques to improve the quality of similarity queries over image databases performing association rule mining over textual descriptions and automatically extracted features of the image content. Based on the knowledge mined, each query posed is rewritten in order to better meet the user expectations. We propose an extension of SQL aimed at exploring mining processes over complex data, generating association rules that extract semantic information from the textual description superimposed to the extracted features, thereafter using them to rewrite the queries. As a result, the system obtains results closer to the user expectation than i…
AN ONTOLOGY-BASED APPROACH TO PROVIDE PERSONNALIZED RECOMMENDATIONS USING A STOCHASTIC ALGORITHM
2011
International audience; The use of personalized recommender systems to assist users in the selection of products is becoming more and more popular and wide-spread. The purpose of a recommender system is to provide the most suitable items from an knowledge base, according the user knowledge, tastes, interests, ... These items are generally proposed as ordered lists. In this article, we propose to combine works from adaptive hypermedia systems, semantic web and combinatory to create a new kind of recommender systems suggesting combinations of items corresponding to the user.
Burden of HIV and hepatitis C co-infection: the changing epidemiology of hepatitis C in HIV-infected patients in France.
2015
Équipe UB/CHU (EA) Pôle MERS CT3 Hors Enjeu; International audience; Background & AimsTo better evaluate the HIV-HCV co-infection burden in the context of new effective HCV treatment. MethodsWe reviewed all the epidemiological data available on HCV-related disease in HIV-infected patients in France. Sources of data have been selected using the following criteria: (i) prospective cohorts or cross-sectional surveys; (ii) conducted at a national level; (iii) in the HIV-infected population; (iv) able to identify HCV co-infection and chronic active hepatitis C (HCV RNA positive); and (v) conducted during the period 2003-2012. ResultsThe overall prevalence of HIV-HCV co-infection has decreased fr…
CasuHal : HAL Open Access Repository Users Group
2018
CasuHal is a team of volunteers, gathered into a non-profit organisation. Its purpose is to foster exchanges and communication among its 270+ members, to help them understand and take ownership of the HAL platform, and to propose the functional developments needed for a full institutional use.
PV-Alert: Fog Computing based Architecture for Safeguarding Vulnerable Road Users
2018
High volumes of pedestrians, cyclists and other vulnerable road users (VRUs) have much higher casualty rates per mile; not surprising given their lack of protection from an accident. In order to alleviate the problem, sensing capabilities of smartphones can be used to detect, warn and safeguard these road users. In this research we propose an infrastructure-less fog-based architecture named PV-Alert (Pedestrian-Vehicle Alert) where fog nodes process delay sensitive data obtained from smartphones for alerting pedestrians and drivers before sending the data to the cloud for further analysis. Fog computing is considered in developing the architecture since it is an emerging paradigm that has p…