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Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Schröder, Anna Marie. "Unboxing The Algorithm : Understandability And Algorithmic Experience In Intelligent Music Recommendation Systems." Thesis, Malmö universitet, Institutionen för konst, kultur och kommunikation (K3), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43841.

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After decades of black-boxing the existence of algorithms in technologies of daily need, users lack confidence in handling them. This thesis study investigates the use situation of intelligent music recommendation systems and explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience. In a Research-Through-Design approach, the project conducted focus user sessions and an expert interview to explore first-hand insights. The analysis showed that users had limited mental models so far but brought curiosity to learn. Explorative

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Robles, Sebastian. "Business intelligence in Chile, recommendations to develop local applications." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/70831.

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Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, June 2011.<br>"February 2010." Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 60).<br>The volume of information generated from enterprise applications is growing exponentially, and the cost of storage is decreasing rapidly. In addition, cloud-based applications, mobile devices and social networks are becoming relevant sources of unstructured data that provide essential information for strategic decisions making. Therefore, with time, enterprise dat

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Thiengburanathum, Pree. "An intelligent destination recommendation system for tourists." Thesis, Bournemouth University, 2018. http://eprints.bournemouth.ac.uk/30571/.

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Choosing a tourist destination from the information available is one of the most complex tasks for tourists when making travel plans, both before and during their travel. With the development of a recommendation system, tourists can select, compare and make decisions almost instantly. This involves the construction of decision models, the ability to predict user preferences, and interpretation of the results. This research aims to develop a Destination Recommendation System (DRS) focusing on the study of machine-learning techniques to improve both technical and practical aspects in DRS. First,

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Xu, Shuting. "Study and Design of an Intelligent Preconditioner Recommendation System." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_diss/327.

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There are many scientific applications in which there is a need to solve very large linear systems. The preconditioned Krylove subspace methods are considered the preferred methods in this field. The preconditioners employed in the preconditioned iterative solvers usually determine the overall convergence rate. However, choosing a good preconditioner for a specific sparse linear system arising from a particular application is the combination of art and science, and presents a formidable challenge for many design engineers and application scientists who do not have much knowledge of preconditio

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Zhang, Junjie. "Development of a consumer-oriented intelligent garment recommendation system." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10026/document.

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Maintenant, l’achat de vêtements sur l’Internet est devenu une tendance importante pour les consommateurs du monde entier. Pourtant, dans les différents systèmes de vente en ligne, il manque systématiquement de recommandations personnalisées, comme celles fournies par les vendeurs d’une boutique physique, afin de proposer les produits les mieux adaptés à des différents consommateurs selon leurs morphotypes et leurs attentes émotionnelles. Dans cette thèse doctorale, nous proposons un système de recommandation orienté vers les consommateurs, qui peut être utilisé, comme un vendeur virtuel, à l’

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Lagerqvist, Gustaf, and Anton Stålhandske. "Recommendation systems for recruitment within an educational context." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42902.

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Alongside the evolution of the recruitment process, different types of recommendation systems have been developed. The purpose of this study is to investigate recommendation systems within educational contexts, successful implementations of recommendation system architecture patterns, and alternatives to previous experience when evaluating candidates. The study is conducted through two separate methods; A literature review with a qualitative approach and design science research methodology focused on design and development, demonstration and evaluation. The literature review shows that, for re

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Lohi, Abdolkhalil. "Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network." Thesis, University of Westminster, 2013. https://westminsterresearch.westminster.ac.uk/item/99060/investigation-of-an-intelligent-personalised-service-recommendation-system-in-an-ims-based-cellular-mobile-network.

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Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part o

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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.

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Durant mes travaux en thèse, nous avons imaginé et poser les briques d'un système de recommandation intelligent (DIRS) orienté vers les créateurs de vêtements afin de les aider à créer des nouveaux produits personnalisés. Pour développer ce système, nous avons dans un premier temps identifié les composants clés du processus de création, puis nous avons créé un ensemble de bases de données pour collecter les données pertinentes. Dans un deuxième temps, nous avons acquis des données anthropométriques, recueilli la perception du concepteur à partir de ces mêmes morphotypes en utilisant un body sc

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Khoshkangini, Reza. "Personalized Game Content Generation and Recommendation for Gamified Systems." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424854.

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Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive mu

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Alsalama, Ahmed. "A Hybrid Recommendation System Based on Association Rules." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1250.

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Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small

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Drushku, Krista. "User intent based recommendation for modern BI systems." Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4001/document.

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Stocker de grandes quantités de données complexifie les interactions avec les systèmes de Business Intelligence (BI). Les systèmes de recommandation semblent un choix logique pour aider les utilisateurs dans leur analyse. Ils extraient des comportements de données historiques et suggèrent des actions personnalisées, potentiellement redondantes, via des scores de similarité. La diversité est essentielle pour améliorer la satisfaction des utilisateurs, d’où l’intérêt particulier accordé aux recommandations complémentaires. Nous avons étudié deux problèmes concrets d’exploration de données en BI

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Lesage, Laurent. "Data analysis for insurance : recommendation system based on a Multivariate Hawkes Process." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0048.

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L'objectif de la thèse est de construire un système de recommandation pour l'assurance. En observant le comportement et l'évolution d'un client dans le contexte assurantiel, les clients semblent modifier leur couverture d'assurance lorsqu'un événement important survient dans leur vie. Afin de prendre en compte l'influence des événements de la vie (par exemple, mariage, naissance, changement d'emploi) sur la sélection des garanties d'assurance auprès des clients, nous modélisons le système de recommandation avec un processus de Hawkes multivarié, qui comprend plusieurs fonctionnalités spécifiqu

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Sun, Runpu. "Using Social Media Intelligence to Support Business Knowledge Discovery and Decision Making." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/145394.

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The new social media sites - blogs, micro-blogs, and social networking sites, among others - are gaining considerable momentum to facilitate collaboration and social interactions in general. These sites provide a tremendous asset for understanding social phenomena by providing a wide availability of novel data sources. Recent estimates suggest that social media sites are responsible for as much as one third of new Web content, in the forms of social networks, comments, trackbacks, advertisements, tags, etc. One critical and immediate challenge facing the MIS researchers then becomes - how to

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14

Casey, Walker Evan. "Scalable Collaborative Filtering Recommendation Algorithms on Apache Spark." Scholarship @ Claremont, 2014. http://scholarship.claremont.edu/cmc_theses/873.

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Collaborative filtering based recommender systems use information about a user's preferences to make personalized predictions about content, such as topics, people, or products, that they might find relevant. As the volume of accessible information and active users on the Internet continues to grow, it becomes increasingly difficult to compute recommendations quickly and accurately over a large dataset. In this study, we will introduce an algorithmic framework built on top of Apache Spark for parallel computation of the neighborhood-based collaborative filtering problem, which allows the algor

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Mittal, Nupur. "Data, learning and privacy in recommendation systems." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S084/document.

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Les systèmes de recommandation sont devenus une partie indispensable des services et des applications d’internet, en particulier dû à la surcharge de données provenant de nombreuses sources. Quel que soit le type, chaque système de recommandation a des défis fondamentaux à traiter. Dans ce travail, nous identifions trois défis communs, rencontrés par tous les types de systèmes de recommandation: les données, les modèles d'apprentissage et la protection de la vie privée. Nous élaborons différents problèmes qui peuvent être créés par des données inappropriées en mettant l'accent sur sa qualité e

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Shapiro, Daniel. "Composing Recommendations Using Computer Screen Images: A Deep Learning Recommender System for PC Users." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36272.

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A new way to train a virtual assistant with unsupervised learning is presented in this thesis. Rather than integrating with a particular set of programs and interfaces, this new approach involves shallow integration between the virtual assistant and computer through machine vision. In effect the assistant interprets the computer screen in order to produce helpful recommendations to assist the computer user. In developing this new approach, called AVRA, the following methods are described: an unsupervised learning algorithm which enables the system to watch and learn from user behavior, a metho

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Olmucci, Poddubnyy Oleksandr. "Graph Neural Networks for Recommender Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25033/.

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In recent years, a new type of deep learning models, Graph Neural Networks (GNNs), have demonstrated to be a powerful learning paradigm when applied to problems that can be described via graph data, due to their natural ability to integrate representations across nodes that are connected via some topological structure. One of such domains is Recommendation Systems, the majority of whose data can be naturally represented via graphs. For example, typical item recommendation datasets can be represented via user-item bipartite graphs, social recommendation datasets by social networks, and so on. T

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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.

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Les systèmes commerciaux de recommandation de vêtements ont été largement utilisés dans l'industrie de l'habillement. Cependant, les recherches existantes sur la conception de vêtements numériques se sont concentrées sur les évolutions techniques du processus de conception virtuelle, avec peu de retours de métier provenant des designers. La coupe d'un vêtement joue un rôle important dans l'achat de celui-ci par le client. Afin de développer un vêtement correctement ajusté, les stylistes et les modélistes doivent ajuster le patron du vêtement plusieurs fois jusqu'à ce que le client soit satisfa

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Nyberg, Selma. "Video Recommendation Based on Object Detection." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-351122.

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In this thesis, various machine learning domains have been combined in order to build a video recommender system that is based on object detection. The work combines two extensively studied research fields, recommender systems and computer vision, that also are rapidly growing and popular techniques on commercial markets. To investigate the performance of the approach, three different content-based recommender systems have been implemented at Spotify, which are based on the following video features: object detections, titles and descriptions, and user preferences. These systems have then been

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Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.

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Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des techn

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Afchar, Darius. "Interpretable Music Recommender Systems." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS608.

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« Pourquoi est-ce qu’on me recommande toujours les même musiques ? » « Pourquoi notre système recommande-t’il cela aux utilisateurs ? » De nos jours, les plateformes de streaming sont le moyen le plus courant d'écouter de la musique enregistrée. Pourtant, les recommandations musicales — au cœur de ces plateformes — sont loin d’être une mince affaire. Il arrive parfois qu’utilisateurs et ingénieurs soient tout aussi perplexes du comportement d’un système de recommandation musicale (SRM). Les SRM ont été utilisés avec succès pour aider à explorer des catalogues comptant des dizaines de millions

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Kaufman, Jaime C. "A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features." UNF Digital Commons, 2014. http://digitalcommons.unf.edu/etd/540.

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Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing

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Falih, Issam. "Attributed Network Clustering : Application to recommender systems." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCD011/document.

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Au cours de la dernière décennie, les réseaux (les graphes) se sont révélés être un outil efficace pour modéliser des systèmes complexes. La problématique de détection de communautés est une tâche centrale dans l’analyse des réseaux complexes. La majeur partie des travaux dans ce domaine s’intéresse à la structure topologique des réseaux. Cependant, dans plusieurs cas réels, les réseaux complexes ont un ensemble d’attributs associés aux nœuds et/ou aux liens. Ces réseaux sont dites : réseaux attribués. Mes activités de recherche sont basées principalement sur la détection des communautés dans

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Gonard, François. "Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS121/document.

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La quantité d'informations, de produits et de relations potentielles dans les réseaux sociaux a rendu indispensable la mise à disposition de recommandations personnalisées. L'activité d'un utilisateur est enregistrée et utilisée par des systèmes de recommandation pour apprendre ses centres d'intérêt. Les recommandations sont également utiles lorsqu'estimer la pertinence d'un objet est complexe et repose sur l'expérience. L'apprentissage automatique offre d'excellents moyens de simuler l'expérience par l'emploi de grandes quantités de données.Cette thèse examine le démarrage à froid en recomman

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Hou, Hailong. "Computing with Granular Words." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/cs_theses/73.

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Computational linguistics is a sub-field of artificial intelligence; it is an interdisciplinary field dealing with statistical and/or rule-based modeling of natural language from a computational perspective. Traditionally, fuzzy logic is used to deal with fuzziness among single linguistic terms in documents. However, linguistic terms may be related to other types of uncertainty. For instance, different users search ‘cheap hotel’ in a search engine, they may need distinct pieces of relevant hidden information such as shopping, transportation, weather, etc. Therefore, this research work focuses

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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.

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Les algorithmes de bandits-manchots pour les systèmes de recommandation sensibles au contexte font aujourd’hui l’objet de nombreuses études. Afin de répondre aux enjeux de cette thématique, les contributions de cette thèse sont organisées autour de 3 axes : 1) les systèmes de recommandation ; 2) les algorithmes de bandits-manchots (contextuels et non contextuels) ; 3) le contexte. La première partie de nos contributions a porté sur les algorithmes de bandits-manchots pour la recommandation. Elle aborde la diversification des recommandations visant à améliorer la précision individuelle. La seco

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Tomasone, Marco Benito. "Pipeline per il Machine Learning: Analisi dei workflow e framework per l’orchestrazione i casi Recommendation System e Face2Face Traslation." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Al giorno d’oggi tantissimi dei problemi che affrontiamo quotidianamente prevedono l’utilizzo di tecniche di Intelligenza Artificiale. Sono nate sempre più techinchè fino al machine learning è una materia in forte sviluppo, e i modelli, gli algoritmi si espandono a vista d’occhio. Spesso molti dei problemi vengono affrontati e risolti tramite pipeline di modelli di machine learning che sequenzializzati fra loro portano alla soluzione sperata. Nascono sempre più piattaforme come Ai4Eu che si pongono come centro di scambio di modelli, dataset e conoscenza. Nasce quindi l'esigenza di voler automa

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Langelaar, Johannes, and Mattsson Adam Strömme. "Federated Neural Collaborative Filtering for privacy-preserving recommender systems." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446913.

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In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. With an additional purpose of training the models without collecting any sensitive data from the users, both models were implemented with a learning technique that does not require the server's knowledge of the users' data, called federated learning. The federated version of Matrix Factorization is already well-researched

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Smith, Matthew Scott. "Implicit Affinity Networks." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1112.

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Although they clearly exist, affinities among individuals are not all easily identified. Yet, they offer unique opportunities to discover new social networks, strengthen ties among individuals, and provide recommendations. We propose the idea of Implicit Affinity Networks (IANs) to build, visualize, and track affinities among groups of individuals. IANs are simple, interactive graphical representations that users may navigate to uncover interesting patterns. This thesis describes a system supporting the construction of IANs and evaluates it in the context of family history and online communiti

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Bahceci, Oktay. "Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210252.

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Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements an

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Sidana, Sumit. "Systèmes de recommandation pour la publicité en ligne." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM061/document.

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Cette thèse est consacrée à l’étude des systèmes de recommandation basés sur des réseaux de neurones artificiels appris pour faire de l'ordonnancement de produits avec des retours implicites (sous forme de clics). Dans ce sens, nous proposons un nouveau modèle neuronal qui apprend conjointement la représentation des utilisateurs et des produits dans un espace latent, ainsi que la relation de préférence des utilisateurs sur les produits. Nous montrons que le modèle proposé est apprenable au sens du principe de la minimisation du risque empirique et performant par rapport aux autres modèles de l

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Sahay, Saurav. "Socio-semantic conversational information access." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42855.

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The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors

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Morbieu, Stanislas. "Leveraging textual embeddings for unsupervised learning." Electronic Thesis or Diss., Université Paris Cité, 2020. http://www.theses.fr/2020UNIP5191.

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Les données textuelles constituent un vivier d'information exploitable pour de nombreuses entreprises. En particulier, le web fournit une source quasiment inépuisable de données textuelles qui peuvent être utilisées à profit pour des systèmes de recommandation, de veille, de recherche d'information, etc. Les récentes avancées en traitement du langage naturel ont permit de capturer le sens des mots dans leur contexte afin d'améliorer les systèmes de traduction, de résumés, ou encore le regroupement de documents suivant des catégories prédéfinies. La majorité de ces applications reposent cependa

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Bhaduri, Sashmit B. "sALERT : an intelligent information alerting and notification web service." Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-05-5870.

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Web services increasingly serve as large repositories and conduits of information. However, they do not always allow for the efficient dissemination of this information, particularly in a reactive way. In this report, I describe sALERT, a web-based application that allows for targeted information from various web services to be combined and cross-referenced in order to produce a system that is more convenient and more efficient in reactively disseminating information. This dissemination is performed using mobile notification mechanisms such as text messages, and information targeting is perfor

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Yang, Cheng-Kun, and 楊政錕. "Intelligent Recommendation System For Weight Control." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58808999379688830836.

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碩士<br>育達商業科技大學<br>資訊管理所<br>98<br>Taiwan in recent years, set off to lose weight boom, major celebrity publicly admitted losing weight. Many people lose weight because obesity does not look good. But in fact, the appearance of unsightly fat is one of the issues. Health threat of obesity is most worrying. There were many medical studies confirm that obesity increases disease morbidity and mortality. Health hazards of obesity are very broad, from head to toe will be affected by obesity. Medical research found that obesity may increase the incidence of cancer, such as female breast cancer, endomet

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Huang, Jhen-Gang, and 黃振綱. "Implementation of Intelligent Recommendation Learning Service System." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/37703725679706553554.

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碩士<br>國立臺中教育大學<br>數位內容科技學系碩士班<br>97<br>As information overload becomes more severe, search agent can no longer fulfill the needs of all users, and this is the beginning of the emergence of recommendation mechanism. In the early days, recommendation mechanism was mostly applied to commercial behavior. Under the same background of information overload, e-Learning expands gradually, and e-Learning materials prevail on the Internet. This is a time when recommendation learning comes into exist. As technology matures, a large amount of various teaching materials have been digitalized and uploaded to

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Wu, Wei Ying, and 吳威穎. "An Intelligent Recommendation System for Personalization Hairstyle." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/12051275344496767239.

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碩士<br>國立屏東科技大學<br>資訊管理系所<br>101<br>In pace with the rapid development of information technology, image recognition technology are widely used in all walks of life, in beauty design-related industries order enhance their own competitiveness to a new status. Therefore, we propose a system structure that according user’s experience to design intuitive operating user interface. We can use computer to achieve virtual hairstyles replace and hairstyles recommended by image recognition technology that can reduce time and cost spend. The study module build a recommended hairstyle system to assist users

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林苡良. "Intelligent Recommendation Methodology and System for Patent Search." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/22003738133300676070.

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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>99<br>Due to the intangible assets have been concerned by enterprise continuously year by year, the importance of patent management also has increased. The patent search is the initial process for the patent management. The engineer of the enterprise search and collect the patents from the related patent databases, to study them for drawing out the R&D policy. However, this work causes great human-cost consuming and time-exhausted. It is not easy for users to grasp the core technology key phrases and correct search method. Therefore, this research develops an in

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Liu, Chi-Yun, and 劉季昀. "Effective Parking Recommendation Service for Intelligent Vehicular Guiding System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/68620940219609724605.

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碩士<br>國立中央大學<br>資訊工程研究所<br>99<br>In this paper, an effective parking recommendation service for vehicular intelligent guiding system is provided with real-time parking lot guiding service for future green city. The system shows drivers a best parking lot recommendation sequence and saves drivers’ time circling around by the accurate prediction of parking probability in each parking lot. The cost model containing parking probability as a factor for the best recommendation sequence is constructed according to the main conditions for parking. Through the collection and analysis of real data from

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Chiu, Ching-Yueh, and 邱敬越. "An Intelligent System for POIs Recommendations on Mobile Devices." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/48063743491665265389.

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碩士<br>淡江大學<br>電機工程學系碩士班<br>103<br>Due to the increasing computational capability of mobile device, mature cloud service and prevalent internet infrastructure, mobile application service is now become a part of our convenient life. However the big data issue will come from the increasing data with these services. For example, user usually spend lots of time searching the information of attraction which match their demand with mobile device during traveling. If the interface on mobile device is in small size, how to provide the information of attraction to user accurately becomes a signific

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Kuo, Tai-Liang, and 郭泰良. "A Reviewer Recommendation System based on Collaborative Intelligence." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/62225427013974464416.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>97<br>In this thesis, expert-finding problem is transformed to a classification issue. We build a knowledge database to represent the expertise characteristic of domain from web information constructed by collaborative intelligence, and an incremental learning method is proposed to update the database. Furthermore, results are ranked by measuring the correlation in the concept network from online encyclopedia. In our experiments, we use the real world dataset which comprise 2,701 experts who are categorized into 8 expertise domains. Our experimental results show that

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Wu, Wei-chih, and 吳偉誌. "The Study of Mobile Intelligence Tourism Recommendation System." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/54060474423201903207.

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碩士<br>國立高雄第一科技大學<br>資訊管理研究所<br>100<br>Due to circumstance of the two days holiday, people gradually pay more attention to the leisure time and tourism. Besides the indoor entertainments, more and more people are willing to stay with the natural beauty. The public pays more attention to the demand and the quality of tourism day by day. Relatively, public and private departments’ approaches to manage tourism are also changing and evolving. In tradition, most of countrymen’s ways of touring and information applying are the combination of plan maps, maps of direct mails, and GPS guiding systems

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Chih-LunChou and 周智倫. "Intelligent Multimedia Content Sharing and Recommendation System in Mobile Environments." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/16317623815215890529.

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博士<br>國立成功大學<br>資訊工程學系碩博士班<br>100<br>This thesis proposes a novel approach to clustering the interests of mobile users, increasing the lifetime of interest groups, and increasing the throughput in mobile user-to-mobile user (M2M) environments for mobile IPTV (MOTV) societies. This thesis develops an interest ontology of cellular automata (CA) clustering using the zone of interest (ZOI) for mobicast communications in mobile ad hoc network (MANET) environments. The key to the proposed method is to integrate CA clustering with the ontology of users’ interests. This thesis proposes that both an in

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Ou, Po-Wen, and 歐博文. "MOOCIRS: A MOOC Intelligent Recommendation System Based on Learning Diagnosis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9eyxru.

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Κόρδαρης, Ιωάννης. "Η αντιμετώπιση της πληροφοριακής υπερφόρτωσης ενός οργανισμού με χρήση ευφυών πρακτόρων". Thesis, 2014. http://hdl.handle.net/10889/7965.

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Η πληροφοριακή υπερφόρτωση των χρηστών αποτελεί βασικό πρόβλημα ενός οργανισμού. Η συσσώρευση μεγάλου όγκου πληροφορίας στα πληροφοριακά συστήματα, προκαλεί στους χρήστες άγχος και υπερένταση, με αποτέλεσμα να δυσχεραίνει την ικανότητά τους για λήψη αποφάσεων. Λόγω αυτού, η επίδραση της πληροφοριακής υπερφόρτωσης στους οργανισμούς είναι καταστροφική και απαιτείται η αντιμετώπισή της. Υπάρχουν διάφοροι τρόποι αντιμετώπισης της πληροφοριακής υπερφόρτωσης όπως τα συστήματα υποστήριξης λήψης αποφάσεων, τα συστήματα φιλτραρίσματος πληροφορίας, οι αποθήκες δεδομένων και άλλες τεχνικές της εξόρυξης δ

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Yang, Ming Jui, and 楊明瑞. "An Intelligent Mobile Ordering Recommendation System - A Case Study of Breakfast Store." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ha2zh2.

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碩士<br>景文科技大學<br>電子工程系電腦與通訊碩士班<br>104<br>Breakfast Store is one of the competitiveness career in Taiwan. There are about twenty four breakfast brand franchisees, and over 10 thousands stores so far. Their number and density are more than convenience store (e.g. 7-11, Welcome, Family). Recently, technology develop fast, and order the meal not only use human but also use the computer. Until Now, the mobile and online service are becoming popular, and more convenience than computer, so it will be a potential system in the feature. The thesis is design an outline system. For customer who using

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Eis, Martin Leroyce. "An intelligent chemical recommendation and applicator control system for site-specific crop management." 1989. http://hdl.handle.net/2097/22479.

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Chiu, Chuan-Feng, and 邱川峰. "An Integrated Negotiation and Recommendation System based on Intelligent Software Agent in Electronic Commerce." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/23629652347487748222.

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博士<br>淡江大學<br>資訊工程學系<br>90<br>The Internet has become a popular medium for information exchange and knowledge delivery. Traditional buying and selling activities also follow the trend, therefore the commerce activities have moved to electronic commerce. However, even with the advents of the World Wide Web, online merchants must know what users want and to increase the sales revenue. So, providing recommendation services is an important strategy for the merchants. We analyze users’ on-line behavior and interests, and recommend to them new or potential products and the analysis mechanism is bas

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Lee, Yi-Chi, and 李宜錡. "Integration of Game and Multi-Agent Theory on Intelligent Recommendation System of Organic Vegetables Planting." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/90514166662004915888.

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碩士<br>育達商業技術學院<br>資訊管理所<br>96<br>Because the people are fastidious more and more regarding the diet healthy demand, therefore has accomplished the organic agriculture starting. But the majority farmers when are engaged in the organic planter, does the regular session faced with - what need to plant to a question to be only then good. Therefore, how effectiveness, and chooses the appropriate planter crops precisely, is direction which is worth discussing. In addition, in all appropriate planter's crops, considered that its relative economic value, takes in various seasons should basis of the pr

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Yun-TzLee and 李韻紫. "ITRS: An Intelligent Touring Recommendation System over the Mobile Social Network Using the Cloud Computing Technique." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/89277506530310971514.

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碩士<br>國立成功大學<br>資訊工程學系<br>102<br>Although many touring recommendation systems work with mobile social network, the delivered posts among users are still not to be utilized well for estimating user’s travel preferences. Besides, with the increasing of users in touring recommendation systems, using the parallel cloud computing can help save executing time and computing resources. However, the processing of estimating user’s travel preferences results in the severe redundancy problem [7][8], because when one user updates his/her travel experiences, the social relationship among all users will be

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