Дисертації з теми "Système de recommandation intelligent"
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Militaru, Dorin. "Technologies Internet, systèmes de recommandations et agents intelligents." Paris, ENSAM, 2004. http://www.theses.fr/2004ENAM0036.
Повний текст джерелаThe spread of information technologies (IT) and the growth of the electronic commerce modified the way in which the companies function, forcing them to adopt flexible structures and to produce more efficiently. This research is interested in the part played by new technologies in the setting-up of recommendation systems and information search systems which tend on many markets to control the commercial trades. More precisely, our main objective is to contribute to a better understanding of the recommendation systems by comparing physical economy and electronic commerce. This thesis can contribute to the comprehension of the changes generated in this field by the use of the electronic commerce and to the development of “intelligent” substitutes to the processes currently used. By doing this, we contribute to the emergence of a new face of electronic commerce on Internet by identifying a series of psychological and economic variables which play an important part in the manner in which the economic agents, in particular the companies, act and react. Our main objectives throughout this thesis are to answer to the following questions: Which is the part played by the recommendation systems in the formation of preferences and which is the efficiency of this type of systems? Do the specific characteristics of Internet as an economic environment modify the answers to the previous question? Are the competitiveness factors of the companies on the “new economy” different from those of the traditional economy? Which are the opportunities associated with the electronic commerce, in particular through the “shopbots” which tend to become a “hard” part of the recommender systems?
Duthil, Benjamin. "De l'extraction des connaissances à la recommandation." Phd thesis, Montpellier 2, 2012. http://tel.archives-ouvertes.fr/tel-00771504.
Повний текст джерелаHong, Yan. "Développement d’un système intelligent d’aide à la création de vêtements personnalisés pour des personnes à morphologie atypique par exploitation de connaissances." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I014/document.
Повний текст джерелаThis PhD research project aims at developing a new Personalized Garment Design Support System (PGDSS) for People with Atypical Morphology (PWAM). This system enables to quickly develop garments adapted to their special Functional, Expressive and Aesthetic (FEA) needs and atypical morphologies. In order to realize the proposed PGDSS, two subsystems are developed: the Personalized Fashion Recommendation System (PFRS) and Virtual 3D-to-2D Garment Prototyping Platform (VGPP). The PFRS is developed for selecting the most relevant personalized garment design solutions in terms of color, fabric and style, while the VGPP enables designers to quickly create virtual garments according to their design criteria (product profiles) and visualize them in order to adjust design parameters. The proposed PGDSS can be fully used online. It can be further connected to a garment e-shopping platform or an offline automatic garment manufacturing system.The design factors for personalized garments have been identified and analyzed in my PhD research. The new products generated by the proposed system will meet the specific demands and functions imposed by people with atypical morphology in terms of ergonomics, biophysics, psychology, aesthetics, comfort and convenience. The proposed system is able to offer more personalized designs at low-cost level for highly customized garment market
Zhang, Junjie. "Development of a consumer-oriented intelligent garment recommendation system." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10026/document.
Повний текст джерелаGarment purchasing through the Internet has become an important trend for consumers of all parts of the world. However, in various garment e-shopping systems, it systematically lacks personalized recommendations, like sales advisors in classical shops, in order to propose the most relevant products to different consumers according to their body shapes and fashion requirements. In this thesis, we propose a consumer-oriented recommendation system, which can be used inside a garment online shopping system like a virtual sales advisor. This system has been developed by integrating the professional knowledge of designers and shoppers and taking into account consumers’ perception on products. Following the shopping knowledge on garments, the proposed system recommends garment products to specific consumers by successively executing three modules, namely 1) the Successful Cases Database Module; 2) the Market Forecasting Module; 3) the Knowledge-based Recommendation Module. Also, another module, called the Knowledge Updating Module.This thesis presents an original method for predicting one or several relevant product profiles from a specific consumer profile. It can effectively help consumers to choose garments from the Internet. Compared with other prediction methods, the proposed method is more robust and interpretable owing to its capacity of treating uncertainty
Guivarch, Valérian. "Prise en compte de la dynamique du contexte pour les systèmes ambiants par systèmes multi-agents adaptatifs." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2461/.
Повний текст джерелаThe ambient systems are composed by many heteregeneous devices, distributed in the environment, and interacting dynamically. So, the person is a central concern of these systems that have to adapt themselves to the users' context. Thos kind of systems are called/named context aware system. However, the strong dynamic of ambient systems makes impossible to design a priori all adaptation rules needed. The learning of the behaviour to give to an ambient system depending of its context, independantly of any a priori knowledge -knowledge about the behaviour he has to learn, about the used data, or about the users preferences- is the challenge to which this thesis tries to answer. The main contribution of this work is the design of the adaptive multi agent system Amadeus. Its objective is to learn a pertinent behaviour for an ambient system based on the observation of the reccuring actions performed by users, and then to determine in which contexts theses actions are performed in order to perform them on behalf of the user. The learning performed by Amadeus is based on the AMAS approach (Adaptive Multi-Agent System), and is local to each device. It consists in distributing and integrating the Amadeus agents to each device of the ambient system, these agents being able to determine locally and cooperatively the good behaviour to assign to the associated device depending of the users actions
Shaikh, Yasir Saleem. "Privacy preserving internet of things recommender systems for smart cities." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS001.
Повний текст джерелаDuring the past decade, the Internet of Things (IoT) technology has revolutionized almost all the fields of daily life and has boosted smart cities. Smart cities use IoT technology to collect various types of sensors’ data and then use such data to offer a variety of applications. Since the smart cities’ applications are used by the citizens, therefore providing the customized recommendation services to the citizens based on their preferences, locations and profiles, as well as by exploiting the IoT data (e.g., traffic congestion and parking occupancy) is of great importance which could be provided by an IoT recommender. However, since the IoT recommender utilizes the private data of citizens (e.g., profiles, preferences and habits), it breaches the privacy of the users because the IoT recommender could track the routines and habits of the users by analyzing the historical database or by analyzing the regular recommendation services it offers. Therefore, it is important to preserve the privacy of the users from the IoT recommender. In this thesis, we propose a novel privacy preserving IoT recommender system for smart cities that provides recommendations by exploiting the IoT data of sensors and by considering various metrics. Our approach is organized in three parts. Firstly, we develop an EU General Data Protection Regulation (GDPR)-compliant IoT recommender system for smart parking system that provides recommendations of parking spots and routes by exploiting the data of parking and traffic sensors. For this, we first propose an approach for the mapping of traffic sensors with route coordinates in order to analyze the traffic conditions (e.g., the level of congestion) on the roadways and then developed an IoT recommender. The IoT recommender has been integrated into the smart parking use case of an H2020 EU-KR WISE-IoT project and has been evaluated by the citizens of Santander, Spain through a prototype. Additionally, we develop an IoT recommender for smart skiing that provides skiing routes comprised of specific types of slopes, as well as the nearest slope. For skiing routes, there does not exist any stable routing engine. Therefore, a novel routing engine for skiing routes was developed. This work has also been integrated into the smart skiing use case of WISE-IoT project. Secondly, although the developed IoT recommender for smart parking is GDPR-compliant, however, it does not fully protect the privacy of users. Because, an indiscriminately sharing of users’ data with untrusted third-party IoT parking recommender system causes a breach of privacy, as user’s behavior and mobility patterns can be inferred by analyzing the past travelling history of users. Therefore, we preserve privacy of users against parking recommender system while analyzing their past parking history using k-anonymity and differential privacy techniques. Lastly, since the smart cities applications are developed in a vertical manner and do not talk/communicate with each other, i.e., each application is developed for a certain scenario which generally does not share data with other smart cities applications. Therefore, we proposed two frameworks for the recommendation services across smart cities applications using social IoT. Firstly, on how social IoT can be used for the recommendation services across smart cities applications, and secondly, we propose another type of communication of social IoT at a global level, i.e., social cross-domain application-to-application communications, that enables smart cities applications to communicate with each other and establish social relationships between them
Dong, Min. "Development of an intelligent recommendation system to garment designers for designing new personalized products." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10025/document.
Повний текст джерелаIn my PhD research project, we originally propose a Designer-oriented Intelligent Recommendation System (DIRS) for supporting the design of new personalized garment products. For developing this system, we first identify the key components of a garment design process, and then set up a number of relevant databases, from which each design scheme can be formed. Second, we acquire the anthropometric data and designer’s perception on body shapes by using a 3D body scanning system and a sensory evaluation procedure. Third, an instrumental experiment is conducted for measuring the technical parameters of fabrics, and five sensory experiments are carried out in order to acquire designers’ knowledge. The acquired data are used to classify body shapes and model the relations between human bodies and the design factors. From these models, we set up an ontology-based design knowledge base. This knowledge base can be updated by dynamically learning from new design cases. On this basis, we put forward the knowledge-based recommendation system. This system is used with a newly developed design process. This process can be performed repeatedly until the designer’s satisfaction. The proposed recommendation system has been validated through a number of successful real design cases
Patel, Namrata. "Mise en œuvre des préférences dans des problèmes de décision." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT286/document.
Повний текст джерелаIntelligent ‘services’ are increasingly used on e-commerce platforms to provide assistance to customers. In this context, preferences have gained rapid interest for their utility in solving problems related with decision making. Research on preferences in AI has shed light on various ways of tackling this problem, ranging from the acquisition of preferences to their formal representation and eventually their proper manipulation. Following a recent trend of stepping back and looking at decision-support systems from the user’s point of view, i.e. designing them on the basis of psychological, linguistic and personal considerations, we take up the task of developing an “intelligent” tool which uses comparative preference statements for personalised decision support. We tackle and contribute to different branches of research on preferences in AI: (1) their acquisition (2) their formal representation and manipulation (3) their implementation. We first address a bottleneck in preference acquisition by proposing a method of acquiring user preferences, expressed in natural language (NL), which favours their formal representation and further manipulation. We then focus on the theoretical aspects of handling comparative preference statements for decision support. We finally describe our tool for product recommendation that uses: (1) a review-based analysis to generate a product database, (2) an interactive preference elicitation unit to guide users to express their preferences, and (3) a reasoning engine that manipulates comparative preference statements to generate a preference-based ordering on outcomes as recommendations
Falih, Issam. "Attributed Network Clustering : Application to recommender systems." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCD011/document.
Повний текст джерелаIn complex networks analysis field, much effort has been focused on identifying graphs communities of related nodes with dense internal connections and few external connections. In addition to node connectivity information that are mostly composed by different types of links, most real-world networks contains also node and/or edge associated attributes which can be very relevant during the learning process to find out the groups of nodes i.e. communities. In this case, two types of information are available : graph data to represent the relationship between objects and attributes information to characterize the objects i.e nodes. Classic community detection and data clustering techniques handle either one of the two types but not both. Consequently, the resultant clustering may not only miss important information but also lead to inaccurate findings. Therefore, various methods have been developed to uncover communities in networks by combining structural and attribute information such that nodes in a community are not only densely connected, but also share similar attribute values. Such graph-shape data is often referred to as attributed graph.This thesis focuses on developing algorithms and models for attributed graphs. Specifically, I focus in the first part on the different types of edges which represent different types of relations between vertices. I proposed a new clustering algorithms and I also present a redefinition of principal metrics that deals with this type of networks.Then, I tackle the problem of clustering using the node attribute information by describing a new original community detection algorithm that uncover communities in node attributed networks which use structural and attribute information simultaneously. At last, I proposed a collaborative filtering model in which I applied the proposed clustering algorithms
Jedor, Matthieu. "Bandit algorithms for recommender system optimization." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.
Повний текст джерелаIn this PhD thesis, we study the optimization of recommender systems with the objective of providing more refined suggestions of items for a user to benefit.The task is modeled using the multi-armed bandit framework.In a first part, we look upon two problems that commonly occured in recommendation systems: the large number of items to handle and the management of sponsored contents.In a second part, we investigate the empirical performance of bandit algorithms and especially how to tune conventional algorithm to improve results in stationary and non-stationary environments that arise in practice.This leads us to analyze both theoretically and empirically the greedy algorithm that, in some cases, outperforms the state-of-the-art
Chi, Cheng. "Personalized pattern recommendation system of men’s shirts based on precise body measurement." Electronic Thesis or Diss., Centrale Lille Institut, 2022. http://www.theses.fr/2022CLIL0003.
Повний текст джерелаCommercial garment recommendation systems have been widely used in the apparel industry. However, existing research on digital garment design has focused on the technical development of the virtual design process, with little knowledge of traditional designers. The fit of a garment plays a significant role in whether a customer purchases that garment. In order to develop a well-fitting garment, designers and pattern makers should adjust the garment pattern several times until the customer is satisfied. Currently, there are three main disadvantages of traditional pattern-making: 1) it is very time-consuming and inefficient, 2) it relies too much on experienced designers, 3) the relationship between the human body shape and the garment is not fully explored. In practice, the designer plays a key role in a successful design process. There is a need to integrate the designer's knowledge and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modelling, and garment fit assessment should be integrated into the apparel CAD system to improve the efficiency of the design process.Based on the above issues, in this thesis, a fit-oriented garment pattern intelligent recommendation system is proposed for supporting the design of personalized garment products. The system works in combination with a newly developed design process, i.e. body shape identification - design solution recommendation - 3D virtual presentation and evaluation - design parameter adjustment. This process can be repeated until the user is satisfied. The proposed recommendation system has been validated by some successful practical design cases
Gutowski, Nicolas. "Recommandation contextuelle de services : application à la recommandation d'évènements culturels dans la ville intelligente." Thesis, Angers, 2019. http://www.theses.fr/2019ANGE0030.
Повний текст джерелаNowadays, Multi-Armed Bandit algorithms for context-aware recommendation systems are extensively studied. In order to meet challenges underlying this field of research, our works and contributions have been organised according to three research directions : 1) recommendation systems ; 2) Multi-Armed Bandit (MAB) and Contextual Multi-Armed Bandit algorithms (CMAB) ; 3) context.The first part of our contributions focuses on MAB and CMAB algorithms for recommendation. It particularly addresses diversification of recommendations for improving individual accuracy. The second part is focused on contextacquisition, on context reasoning for cultural events recommendation systems for Smart Cities, and on dynamic context enrichment for CMAB algorithms
Lemdani, Roza. "Système hybride d'adaptation dans les systèmes de recommandation." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC050/document.
Повний текст джерелаRecommender systems are tools used to present users with items that might interest them. Such systems use algorithms that rely on the domain application. These algorithms are then executed for each user in order to find the most relevant recommendations for him, without taking into account his specific needs.In this thesis, we define a hybrid recommender system which combines several recommendation algorithms in order to obtain more accurate recommendations. Moreover, the defined approach relies on the structure of the input ontology, which makes the framework reusable, adaptable and domain-independent (music, research papers, films, etc.).We also had an interest in detecting in which kind of recommendations a user responds better in order to adapt the recommendation process to each user category and obtain more targeted recommendations. Finally, our approach can explain each recommendation, which increases the user confidence in the system by proving him that the recommendations are adapted to him. We also allow the user to correct the explanations in order to help the system to get a better understanding of him and avoid non accurate recommendations in the future.Our recommender system has been experimented online with real users and offline by performing a cross-validation on the MovieLens dataset. The results of the experimentation are very satisfying so far
Piton, Thomas. "Une Méthodologie de Recommandations Produits Fondée sur l'Actionnabilité et l'Intérêt Économique des Clients - Application à la Gestion de la Relation Client du groupe VM Matériaux." Phd thesis, Université de Nantes, 2011. http://tel.archives-ouvertes.fr/tel-00643243.
Повний текст джерелаSidana, Sumit. "Systèmes de recommandation pour la publicité en ligne." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM061/document.
Повний текст джерелаThis thesis is dedicated to the study of Recommendation Systems for implicit feedback (clicks) mostly using Learning-to-rank and neural network based approaches. In this line, we derive a novel Neural-Network model that jointly learns a new representation of users and items in an embedded space as well as the preference relation of users over the pairs of items and give theoretical analysis. In addition we contribute to the creation of two novel, publicly available, collections for recommendations that record the behavior of customers of European Leaders in eCommerce advertising, Kelkoofootnote{url{https://www.kelkoo.com/}} and Purchfootnote{label{purch}url{http://www.purch.com/}}. Both datasets gather implicit feedback, in form of clicks, of users, along with a rich set of contextual features regarding both customers and offers. Purch's dataset, is affected by popularity bias. Therefore, we propose a simple yet effective strategy on how to overcome the popularity bias introduced while designing an efficient and scalable recommendation algorithm by introducing diversity based on an appropriate representation of items. Further, this collection contains contextual information about offers in form of text. We make use of this textual information in novel time-aware topic models and show the use of topics as contextual information in Factorization Machines that improves performance. In this vein and in conjunction with a detailed description of the datasets, we show the performance of six state-of-the-art recommender models.Keywords. Recommendation Systems, Data Sets, Learning-to-Rank, Neural Network, Popularity Bias, Diverse Recommendations, Contextual information, Topic Model
Szczerbak, Michal Krzysztof. "Colloborative Situation Awareness." Télécom Bretagne, 2013. http://www.telecom-bretagne.eu/publications/publication.php?idpublication=13949.
Повний текст джерелаSituation awareness and collective intelligence are two technologies used in smart systems. The former renders those systems able to reason upon their abstract knowledge of what is going on. The latter enables them learning and deriving new information from a composition of experiences of their users. In this dissertation we present a doctoral research on an attempt to combine the two in order to obtain, in a collaborative fashion, situation-based rules that the whole community of entities would benefit of sharing. We introduce the KRAMER recommendation system, which we designed and implemented as a solution to the problem of not having decision support tools both situation-aware and collaborative. The system is independent from any domain of application in particular, in other words generic, and we apply its prototype implementation to context-enriched social communication scenario
Lherisson, Pierre-René. "Système de recommandation équitable d'oeuvres numériques. En quête de diversité." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSES018/document.
Повний текст джерелаRecommender systems play a leading role in user’s choice guidance. The search of accuracy in such systems is generally done through an optimization of a function between the items and the users. It has been proved that maximizing only the accuracy does not produce the most useful recommendation for the users. This can confine individuals inside the bubble of their own choices. Additionally, it tends to emphasize the agglomaration of the users’ behavior on few popular items. Thus, it produces a lack of diversity and novelty in recommendations and a limited coverage of the platform catalog. This can lead to an absence of discovery. Monotony and frustration are also induced for the users. This non-discovery is even more crucial if the platform wants to be fair in its recommendations with all contents’ producers (e.g, music artists, writers, video game developers or videographers). The non diversity, and novelty problem is more important for the users because it has been shown that human mind appreciates when moved outside of its comfort zone. For example, the discovery of new artists, the discovery of music genres for which he is not accustomed. In this thesis we present two families of model that aim to go beyond accuracy in content based recommender system scenario. Our two models are based on a user profile understanding prior to bring diversification. They capture the diversity in the user profile and respond to thisdiversity by looking to create a diverse list of recommendation without loosing to much accuracy. The first model is mainly built upon a clustering approach, while the second model is based on an wavelet function. This wavelet function in our model helps us delimit an area where the user will find item slightly different from what he liked in the past. This model is based on the assumption of the existence of a defined intermediate area between similar and different items. This area is also suitable for discovery. Our proposals are tested on a common experimental design that consider well-known datasets and state-of-the-art algorithm. The results of our experiments show that our approaches indeed bring diversity and novelty and are also competitive against state-of-the-art method. We also propose a user-experiment to validate our model based on the wavelet. The results of user centered experiments conclude that this model corresponds with human cognitive and perceptual behavior
Cambolive, Guillaume. "Scrables : un système intelligent d'audit." Toulouse 3, 1993. http://www.theses.fr/1993TOU30237.
Повний текст джерелаKaroui, Hajer. "Système coopératif de type égal-à-égal pour la recommandation : Application à la gestion et la recommandation de références bibliographiques." Phd thesis, Université Paris-Nord - Paris XIII, 2007. http://tel.archives-ouvertes.fr/tel-00299935.
Повний текст джерелаDeux problématiques se présentent : comment obtenir les références pertinentes et comment choisir des agents avec qui collaborer ? Pour résoudre ces problèmes, nous nous sommes basés sur l'exploitation des historiques des interactions entre les agents.
Le RàPC est utilisée pour deux finalités :
a)déterminer pour une requête, des agents intéressants à interroger ;
b)chercher pour une requête, des références pertinentes.
Grossetti, Quentin. "Système de recommandation sur les plateformes de micro-blogging et bulles filtrantes." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS304.
Повний текст джерелаWith the unprecedented growth of user-generated content produced on microblogging platforms, finding interesting content for a given user has become a major issue. However due to the intrinsic properties of microblogging systems, such as the volumetry, the short lifetime of posts and the sparsity of interactions between users and content, recommender systems cannot rely on traditional methods, such as collaborative filtering matrix factorization. After a thorough study of a large Twitter dataset, we present a propagation model which relies on homophily to propose post recommendations. Our approach relies on the construction of a similarity graph based on retweet behaviors on top of the Twitter graph. We then conduct experiments on our real dataset to demonstrate the quality and scalability of our method. Finally, we investigate community detection algorithms and we present a metric to compute the strength of the filter bubble. Our results show that filter bubble effects are in fact limited for a majority of users. We find that, counter-intuitively, in most cases recommender systems tend to open users perspectives. However, for some specific users, the bubble effect is noticeable and we propose a model relying on communities to provide a list of recommendations closer to the user’s usage of the platform
Fomba, Soumana. "Décision multicritère : un système de recommandation pour le choix de l'opérateur d'agrégation." Thesis, Toulouse 1, 2018. http://www.theses.fr/2018TOU10009/document.
Повний текст джерелаRecommendation systems are becoming more popular. This PhD focusses on MultiCriteriaDecision Analysis (MCDA). For MCDA, it exists multiplication lot of aggregation methods. This diversity of aggregation methods and decision-making situations means that there is no super method applicable in all decision-making situations. The question then is how to choose an appropriate aggregation operator for a given decision problem? In this thesis, we try to have some answers to this question, on the one hand by studying the decision support systems, on the other hand by analyzing different aggregation operators present in the literature. This allowed us to set up a recommendation system implementing several aggregation operators. During an aggregation procedure, the user has the possibility of choosing an aggregation operator from among the available operators. It can also be offered an aggregation operator by the system. The aggregation operator most appropriate to the decision-maker's decision problem is chosen according to several parameters
Meyer, Frank. "Systèmes de recommandation dans des contextes industriels." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00767159.
Повний текст джерелаBolan, Frigo Luciana. "MathTutor : un système tuteur intelligent distribué." Toulouse 1, 2007. http://www.theses.fr/2006TOU10020.
Повний текст джерелаFrainay, Clément. "Système de recommandation basé sur les réseaux pour l'interprétation de résultats de métabolomique." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30297/document.
Повний текст джерелаMetabolomics allows large-scale studies of the metabolic profile of an individual, which is representative of its physiological state. Metabolic markers characterising a given condition can be obtained through the comparison of those profiles. Therefore, metabolomics reveals a great potential for the diagnosis as well as the comprehension of mechanisms behind metabolic dysregulations, and to a certain extent the identification of therapeutic targets. However, in order to raise new hypotheses, those applications need to put metabolomics results in the light of global metabolism knowledge. This contextualisation of the results can rely on metabolic networks, which gather all biochemical transformations that can be performed by an organism. The major bottleneck preventing this interpretation stems from the fact that, currently, no single metabolomic approach allows monitoring all metabolites, thus leading to a partial representation of the metabolome. Furthermore, in the context of human health related experiments, metabolomics is usually performed on bio-fluid samples. Consequently, those approaches focus on the footprints left by impacted mechanisms rather than the mechanisms themselves. This thesis proposes a new approach to overcome those limitations, through the suggestion of relevant metabolites, which could fill the gaps in a metabolomics signature. This method is inspired by recommender systems used for several on-line activities, and more specifically the recommendation of users to follow on social networks. This approach has been used for the interpretation of the metabolic signature of the hepatic encephalopathy. It allows highlighting some relevant metabolites, closely related to the disease according to the literature, and led to a better comprehension of the impaired mechanisms and as a result the proposition of new hypothetical scenario. It also improved and enriched the original signature by guiding deeper investigation of the raw data, leading to the addition of missed compounds. Models and data characterisation, alongside technical developments presented in this thesis, can also offer generic frameworks and guidelines for metabolic networks topological analysis
Jelassi, Mohamed Nidhal. "Un système personnalisé de recommandation à partir de concepts quadratiques dans les folksonomies." Thesis, Clermont-Ferrand 2, 2016. http://www.theses.fr/2016CLF22693/document.
Повний текст джерелаRecommender systems are now popular both commercially as well as within the research community, where many approaches have been suggested for providing recommendations. Folksonomies' users are sharing items (e.g., movies, books, bookmarks, etc.) by annotating them with freely chosen tags. Within the Web 2.0 age, users become the core of the system since they are both the contributors and the creators of the information. In this respect, it is of paramount importance to match their needs for providing a more targeted recommendation. For such purpose, we consider a new dimension in a folksonomy classically composed of three dimensions and propose an approach to group users with close interests through quadratic concepts. Then, we use such structures in order to propose our personalized recommendation system of users, tags and resources. We carried out extensive experiments on two real-life datasets, i.e., MovieLens and BookCrossing which highlight good results in terms of precision and recall as well as a promising social evaluation. Moreover, we study some of the key assessment metrics namely coverage, diversity, adaptivity, serendipity and scalability. In addition, we conduct a user study as a valuable complement to our evaluation in order to get further insights. Finally, we propose a new algorithm that aims to maintain a set of triadic concepts without the re-scan of the whole folksonomy. The first results comparing the performances of our proposition andthe running from scratch the whole process over four real-life datasets show its efficiency
Alchiekh, Haydar Charif. "Les systèmes de recommandation à base de confiance." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0203/document.
Повний текст джерелаRecommender systems (RS) exploit users' behaviour to recommend to them items they would appreciate. Users Behavioral divergence on the web results in a problem of performance fluctuations to (RS). This problem is observed in the approach of collaborative filtering (CF), which exploites the ratings attributed by users to items, and in the trust-based approach (TRS), which exploites the trust relations between the users. We propose a hybrid approach that increases the number of users receiving recommendation, without significant loss of accuracy. Thereafter, we identify several behavioral characteristics that define a user profile. Then we classify users according to their common behavior, and observe the performance of the approaches by class. Thereafter, we focus on the TRS. The concept of trust has been discussed in several disciplines. There is no real consensus on its definition. However, all agree on its positive effect. Subjective logic (LS) provides a flexible platform for modeling trust. We use it to propose and compare three trust models, which aims to predict whether a user source can trust a target user. Trust may be based on the personal experience of the source (local model), or on a system of mouth (collective model), or the reputation of the target (global model). We compare these three models in terms of accuracy, complexity, and robustness against malicious attacks
Gasmi, Bernadette. "Safir : système d'assemblage flexible intelligent multi-robots." Toulouse, ENSAE, 1990. http://www.theses.fr/1990ESAE0014.
Повний текст джерелаDelecroix, Fabien. "Dialoguer pour décider : recommandation experte proactive et prise de décision multi-agents équitable." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10011/document.
Повний текст джерелаIf decision making can be a pure individual process, it can involve several actors and present social aspects. In this thesis, I consider two types of social decision process : supported decision making and collective decision making. Concerning supported decision making, two actors have distinct roles : the decision maker and the assistant. Here, the decision maker is a human agent and the assistant a software one. In many applications, the dialogical abilities of the assistant are deceptive and the dialogue lacks of consistency. To tackle this problem, we design a proactive dialogical agent aiming for the credibility in conversation and the relevance of recommandations : our agent leads the conversation in asking relevant questions to collect the preferences of the decision maker and use them in recommending the alternatives that fit the most. We apply our approach on the e-commerce field. The second contribution concerns collective decision. The objective is to define a process that lead to a fair agreement, even if participants have incomplete preferences. For this purpose, I define the fair agreements by applying the leximax criterion on the rank of alternatives. Then, I propose a negotiation protocol to reach such agreements and the strategy is taken into account to evaluate it. Finally, the protocol is applied to the search of a meeting point in a maze
Benouaret, Idir. "Un système de recommandation contextuel et composite pour la visite personnalisée de sites culturels." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2332/document.
Повний текст джерелаOur work concerns systems that help users during museum visits and access to cultural heritage. Our goal is to design recommender systems, implemented in mobile devices to improve the experience of the visitor, by recommending him the most relevant items and helping him to personalize the tour he makes. We consider two mainly domains of application : museum visits and tourism. We propose a context-aware hybrid recommender system which uses three different methods : demographic, semantic and collaborative. Every method is adapted to a specific step of the museum tour. First, the demographic approach is used to solve the problem of the cold start. The semantic approach is then activated to recommend to the user artworks that are semantically related to those that the user appreciated. Finally, the collaborative approach is used to recommend to the user artworks that users with similar preferences have appreciated. We used a contextual post filtering to generate personalized museum routes depending on artworks which were recommended and contextual information of the user namely : the physical environment, the location as well as the duration of the visit. In the tourism field, the items to be recommended can be of various types (monuments, parks, museums, etc.). Because of the heterogeneous nature of these points of interest, we proposed a composite recommender system. Every recommendation is a list of points of interest that are organized in a package, where each package may constitute a tour for the user. The objective is to recommend the Top-k packages among those who satisfy the constraints of the user (time, cost, etc.). We define a scoring function which estimates the quality of a package according to three criteria : the estimated appreciation of the user, the popularity of points of interest as well as the diversity of packages. We propose an algorithm inspired by composite retrieval to build the list of recommended packages. The experimental evaluation of the system we proposed using a real world data set crawled from Tripadvisor demonstrates its quality and its ability to improve both the relevance and the diversity of recommendations
Tadlaoui, Mohammed. "Système de recommandation de ressources pédagogiques fondé sur les liens sociaux : Formalisation et évaluation." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI053/document.
Повний текст джерелаWith the increasing amount of educational content produced daily by users, it becomes very difficult for learners to find the resources that are best suited to their needs. Recommendation systems are used in educational platforms to solve the problem of information overload. They are designed to provide relevant resources to a learner using some information about users and resources. The present work fits in the context of recommender systems for educational resources, especially systems that use social information. We have defined an educational resource recommendation approach based on research findings in the area of recommender systems, social networks, and Technology-Enhanced Learning. We rely on social relations between learners to improve the accuracy of recommendations. Our proposal is based on formal models that calculate the similarity between users of a learning environment to generate three types of recommendation, namely the recommendation of 1) popular resources; 2) useful resources; and 3) resources recently consulted. We have developed a learning platform, called Icraa, which integrates our recommendation models. The Icraa platform is a social learning environment that allows learners to download, view and evaluate educational resources. In this thesis, we present the results of an experiment conducted for almost two years on a group of 372 learners of Icraa in a real educational context. The objective of this experiment is to measure the relevance, quality and usefulness of the recommended resources. This study allowed us to analyze the user’s feedback on the three types of recommendations. This analysis is based on the users’ traces which was saved with Icraa and on a questionnaire. We have also performed an offline analysis using a dataset to compare our approach with four base line algorithms
Picot-Clémente, Romain. "Une architecture générique de Systèmes de recommandation de combinaison d'items : application au domaine du tourisme." Phd thesis, Université de Bourgogne, 2011. http://tel.archives-ouvertes.fr/tel-00688994.
Повний текст джерелаYou, 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.
Повний текст джерелаPersonalized 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
Draidi, Fady. "Recommandation Pair-à-Pair pour Communautés en Ligne à Grande Echelle." Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2012. http://tel.archives-ouvertes.fr/tel-00766963.
Повний текст джерелаEl, Guedria Sgaier Zina. "Assistance à la recherche documentaire par une approche adaptative à base d’agents et d’artefacts." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR24/document.
Повний текст джерелаThe development and multiplication of information systems and platforms for information access has been accentuated over the past thirty years. The large volume of information available has raised many scientific challenges in different areas such as information retrieval. To access documents grouped in a digital corpus, one must be able to express his/her information need, often in the form of a query, to associate the relevant documents and present them in the best possible way to users. Document research in a thematic digital corpus presenting a high level of technicality in the concerned discipline can be considered as a browsing process driven by some information needs. Such browses requires the use of traditional information retrieval tools to select relevant documents based on a query But they can be improved by the use of customization and adaptation mechanisms in order to refine the representation of information needs according to the specificities of a user, his current browsing or the corpus considered. Indeed, access to digital documents raises problems related to the search for information, the visualization of the results of a query and the browsing between the documents. The process of information retrieval requires to be improved and especially by the integration of the user as a main factor to take into account in the search for satisfaction of his/her information needs. We consider several approaches to help users in their search for documents. A first assistance concerns the reformulation of queries by targeting an audience of users unfamiliar with the technical terms of the field and struggling to express in the form of a query their need. The second approach that we propose is not to consider the user in isolation but to bring it closer to those who have expressed similar research to find the documents they considered relevant. Finally, we include works from the field of the recommendation to better understand the informational needs of the user and help them find what they are looking for by recommending documentary resources. In this thesis, we propose to treat this diversity of influence by a multi-agent system interacting with a shared environment representing the users browsing so that the system may be adapted to use either assistance facilities according to the user's expertise. We applied our work for document research in a digital corpus of legal documents
Dudognon, Damien. "Diversité et système de recommandation : application à une plateforme de blogs à fort trafic (convention CIFRE n°20091274)." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2546/.
Повний текст джерелаRecommender Systems aim at automatically providing objects related to user's interests. These tools are increasingly used on content platforms to help the users to access information. In this context, user's interests can be modeled from the visited content and/or user's actions (clicks, comments, etc). However, these interests can not be modeled for an unknown user (cold start issue). Therefore, modeling is complex and recommendations are often far away from the real user's interests. In addition, existing approaches are generally not able to guarantee good performances on platforms with high trafic and which host a significant volume of data. To obtain more relevant recommendations for each user, we propose a recommender system model that builds a list of recommendations aiming at covering a large range of interests, even when only few information about the user is available. The recommender system model we propose is based on diversity. It uses different interest measures and an aggregation function to build the final set of recommendations. We demonstrate the interest of our approach using reference collections and through a user study. Finally, we evaluate our model on the OverBlog platform to validate its scalability in an industrial context
Aleksandrova, Marharyta. "Factorisation de matrices et analyse de contraste pour la recommandation." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0080/document.
Повний текст джерелаIn many application areas, data elements can be high-dimensional. This raises the problem of dimensionality reduction. The dimensionality reduction techniques can be classified based on their aim: dimensionality reduction for optimal data representation and dimensionality reduction for classification, as well as based on the adopted strategy: feature selection and feature extraction. The set of features resulting from feature extraction methods is usually uninterpretable. Thereby, the first scientific problematic of the thesis is how to extract interpretable latent features? The dimensionality reduction for classification aims to enhance the classification power of the selected subset of features. We see the development of the task of classification as the task of trigger factors identification that is identification of those factors that can influence the transfer of data elements from one class to another. The second scientific problematic of this thesis is how to automatically identify these trigger factors? We aim at solving both scientific problematics within the recommender systems application domain. We propose to interpret latent features for the matrix factorization-based recommender systems as real users. We design an algorithm for automatic identification of trigger factors based on the concepts of contrast analysis. Through experimental results, we show that the defined patterns indeed can be considered as trigger factors
Soualah, Alila Fayrouz. "CAMLearn* : une architecture de système de recommandation sémantique sensible au contexte : application au domaine du m-learning." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS032/document.
Повний текст джерелаGiven the rapid emergence of new mobile technologies and the growth of needs of a moving society in training, works are increasing to identify new relevant educational platforms to improve distant learning. The next step in distance learning is porting e-learning to mobile systems. This is called m-learning. So far, learning environment was either defined by an educational setting, or imposed by the educational content. In our approach, in m-learning, we change the paradigm where the system recommends content and adapts learning follow to learner's context
Guez, Stéphane. "Interix : conception et réalisation d'un système d'aide intelligent sur Unix." Châtenay-Malabry, Ecole centrale de Paris, 1987. http://www.theses.fr/1987ECAP0027.
Повний текст джерелаLabbé, Vincent. "Modélisation et apprentissage des préférences appliqués à la recommandation dans les systèmes d'impression." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2009. http://tel.archives-ouvertes.fr/tel-00814267.
Повний текст джерелаLaurin, Éric. "Système intelligent d'assistance à la perception dans la conduite de véhicule." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0033/MQ67298.pdf.
Повний текст джерелаWong, Tao. "Méthode de conception d'environnement et de laboratoire d'un système tutoriel intelligent." Mémoire, Université de Sherbrooke, 2004. http://savoirs.usherbrooke.ca/handle/11143/4614.
Повний текст джерелаTheljeoui, Adel. "Système hybride de localisation des personnes âgées dans un habitat intelligent." Thesis, Toulouse 2, 2017. http://www.theses.fr/2017TOU20123.
Повний текст джерелаOver the past decade, more and more elderly people are choosing to live alone. Therefore, in order to provide them with continuous home assistance, the notion of "intelligent home" has emerged. Our aim is to combine three technologies (Bluetooth Low Energy, Audio and LiFi) to provide an efficient and accurate hybrid indoor localization system that locates an elderly person inside a smart home. The principle of hybridization of these three subsystems is based on the combination of their respective results by proposing three new DOP-Like metrics to evaluate "precision" and "accuracy" of the result of each subsystem. This evaluation serves to constitute a weighting of the intermediate results in order to calculate the final position of the target to be localized. Thanks to the introduction of these indicators, the localization error of our system decreased from an average of 0.5m to 0.2m
Tounsi, Dhouib Molka. "Ingénierie des connaissances dans le domaine du sourcing pour la recommandation de prestataires." Thesis, Université Côte d'Azur, 2021. http://www.theses.fr/2021COAZ4024.
Повний текст джерелаThis CIFRE doctoral thesis is part of a collaborative research project between the I3S laboratory of the University of Côte d'Azur and the Silex company, and addresses the field of recommendation systems. Silex is a start-up that develops a Software-as-a-Service sourcing tool that allows companies to provide a description of their professional activities, their offers and/or the services they are looking for in natural language (currently French).In this context, the objective of this thesis is to propose a decision support system by exploiting the semantic knowledge that are extracted from the textual descriptions of requests for services and providers, in order to recommend relevant providers for a service request.The contributions of this thesis are the following. First, we proposed a vocabulary for the sourcing field by reusing and integrating existing vocabularies, in order to semantically annotate the textual descriptions of providers and requests for services. Second, we proposed an automatic alignment method to establish the correspondence between different concepts of the considered vocabularies. This approach is based on rules exploiting embedding space and measurements on groups of labels to discover the relationships between concepts. Third, we proposed an algorithm for extracting named entities from the textual descriptions of service requests and providers, and an algorithm for semantic annotation of these descriptions, based on the linking of the extracted entities with the concepts of the defined vocabulary.Fourth, we proposed a provider recommendation algorithm that exploits these knowledges extracted.Finally, we studied the contribution of using ontological knowledge to improve our decision support system for the sourcing domain in order to recommend relevant providers for a service request.The contributions of this thesis are the following. First, we proposed a vocabulary for the sourcing field in order to semantically annotate the textual descriptions of providers and requests for services. This vocabulary was built by reusing and integrating existing vocabularies. Second, we proposed an automatic alignment method to establish the correspondence between different concepts of the considered vocabularies. This approach is based on rules exploiting embedding space and measurements on groups of labels to discover the relationships between concepts. Third, we proposed an algorithm for extracting named entities from the textual descriptions of service requests and providers, and an algorithm for semantic annotation of these descriptions, based on the linking of the extracted entities with the concepts of the defined vocabulary.Fourth, we proposed a provider recommendation algorithm that exploits these knowledge extracted.Finally, we studied the contribution of using ontological knowledge to improve our decision support system for the sourcing domain
Aouag, Sofiane. "Individualisation de l'apprentissage dans un Système Tuteur Intelligent : cas de l'apprentissage de la lecture dans un système AMICAL." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2010. http://tel.archives-ouvertes.fr/tel-00658846.
Повний текст джерелаMartin, Arnaud. "Évolution de profils multi-attributs, par apprentissage automatique et adaptatif dans un système de recommandation pour l'aide à la décision." Toulouse 3, 2012. http://thesesups.ups-tlse.fr/1753/.
Повний текст джерелаConsidering user profiles and their evolutions, for decision support is currently in the community of DSS (Decision Support Systems) an important issue. Indeed, the inclusion of context in the decision is currently emerging for DSS. Indeed the system offers advice to users based on their profile, which represents their preferences through a list of valued criteria. The main constraints come from the fact that the system need to continuously bring relevant information. It therefore requires changing user profiles thanks to their actions. So, the system must not only "understand" what the user likes, but also why. The users' assistance will evolve over time and therefore with the user. Thus the user has at his disposal a kind of personal assistant. The objective of this work is to provide assistance to the user's activity according to his profile. The objective is to develop an algorithm based on automatic techniques, in order to change the profile of a user based on his actions. The assistance provided to the user by the system will evolves according to the evolution of its profile. The problem addressed to the user is a problem of decision making. For this problem, assistance is provided to the user, and it is a refinement of potential solutions. This refining is done through the establishment of scalable scheduling solutions that are presented to the user depending on his / her profile. The realization of such a system requires the articulation of the three main areas of research which are the Multi-Criteria Decision Support, the Disaggregation and Aggregation of preferences, and Machine Learning. The fields of Decision Support and Multi Disaggregation and Aggregation preference can also be assembled as Multi-Criteria Aggregation Process (PAMC). Some methods of Multicriteria Decision Support are set up here and use profile data to provide the best possible support to the user. The decomposition is used to characterize an object to provide data to the learning algorithm required for its operation. Aggregation serves to score an object according to the user profile in order to rank the selected items. Machine Learning is used to change user profiles in order to always have a profile representing as closely as possible the preferences of users. Indeed user preferences change over the time, it is necessary to address these changes in order to adapt the answers to the user. The contributions of this thesis are firstly, the definition, construction and evolution of a user profile (evolutionary profiling) based on explicit and implicit user's actions. This evolutionary profiling is implemented within a recommender system usable without learning base, synchronously and completely incremental, and that allows users to quickly change their preferences and even to be inconsistent (bounded rationality). This system, which complements an Information System Research, aims to establish a total order on a list of items proposed to the user (ranking) and in accordance with his preferences. These also include the definition of techniques used to make parts of solutions to technological challenges as the disintegration of criteria and the inclusion of a variable number of criteria in the process of interactive decision support, and this without firstly defining coherent family of criteria on which the decision is based. Several application frameworks have been developed to evaluate the system and compare it to other systems, but also to test its performance with real user data in an offline mode, and in an online mode using directly the system
Lé, Tang Ho. "Planification de l'enseignement individualisé dans un système tutoriel intelligent à grande échelle." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0021/NQ43495.pdf.
Повний текст джерелаPouchairet-Ramona, Jean-Laurent. "Développement d'un système d'initiation pyrotechnique, sécurisé, autonome, intelligent et intégrant des nanothermites." Thesis, Toulouse, INSA, 2019. http://www.theses.fr/2019ISAT0001/document.
Повний текст джерелаAnswering a growing need for standardization and adaptability in pyrotechnics, we hereby present a smart and safe pyrotechnical infrared (IR) flare electronically controllable through an embedded miniature initiation system. The countermeasure has been designed to fit within a 1”×1”×8” standard cartridge, and consists of three distinct blocks, which are mechanically and electronically interconnected: (1) a pyrotechnical ejection block integrating three ejection charges in a single metalized plastic casing, (2) a micro-initiation stage comprising nanothermite-based micro-initiators and a structured pyrotechnic loaf, (3) a STANAG 4187 compatible electronic control, command and power management block.Throughout this work, we developed a lumped parameter internal ballistics model for the ejection, and conducted a response surface methodology study to extract optimal design parameters. We developed a geometric regression script, based on level set techniques, to model the combustion of multicomponent, sequentially-initiated, partially inerted pyrotechnic loafs. We demonstrated, theoretically then experimentally, that we could control the combustion of IR pyrotechnic loaves using sequential initiation, and that we could control the ejection velocity of IR flares using multipoint mortar ejectors.This work resulted in integrating said technological block in a functional 1’’1’’8’’ controllable, autonomous safe and smart infrared flare demonstrator, CASSIS
El, Dahshan Kamal. "Le développement d'un système intelligent de CAO dans un environnement orienté objet." Compiègne, 1990. http://www.theses.fr/1990COMPD240.
Повний текст джерелаClassical CAD systems fail to offer the designer the intelligent aid that he needs. Furthermore, their complexity prevents from integrating easily the needed procedures that could help the user efficiently, although most of the design work is nothing but modifications. We show, in presenting OPAL, (Object Oriented Programming Assembly) a new system for the design and modification of the assembly of mechanical parts based on the constraint propagation mechanism in an object oriented programming environment. Such a system frees the user from the ancillary tasks of tracking consequences of modifications and of verifications of constraints on the computed results. In so doing, graphical aspects are nothing but a secondary task that can be automatized, letting the designer concentrate on the conceptual decisions. Furthermore, the solution is totally open and can be developed along several axes. We give an idea of the representation we use and of the programming mechanism by developing a small example involving a bolted assembly. The thesis begins with a general introduction of relative aspects to the intelligent CAD systems. The analysis of CAD systems presented in chapter 2 is followed by a more detailed study of the introduction of intelligence in CAD systems, in the third chapter and of a presentation of some existing systems in the fourth one. The necessary techniques for developing our system are presented in chapter five, as well as necessary techniques for knowledge representation in OPAL. The sixth chapter presents implementation details of OPAL. This is followed by our conclusion and propositions for further work
Defude, Bruno. "Etude et réalisation d'un système intelligent de recherche d'informations : le prototype Iota." Grenoble INPG, 1986. http://tel.archives-ouvertes.fr/tel-00321461.
Повний текст джерелаLauffenburger, Jean-Philippe. "Contribution à la surveillance temps-réel du système "Conducteur - Véhicule - Environnement" : élaboration d'un système intelligent d'aide à la conduite." Phd thesis, Université de Haute Alsace - Mulhouse, 2002. http://tel.archives-ouvertes.fr/tel-00732949.
Повний текст джерелаDans ce contexte, la localisation du véhicule et particulièrement les informations de l'environnement d'évolution doivent être pertinentes. Elles sont obtenues grâce à une base de données cartographique spécifiquement développée dans le cadre de ces travaux. Celle-ci est caractérisée par une précision supérieure à celle des bases de données traditionnellement employées dans des dispositifs de navigation.