Rozprawy doktorskie na temat „Large Scale Recommendation”
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Nilsen, John Eirik Bjørhovde. "Large-Scale User Click Analysis in News Recommendation". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23004.
Pełny tekst źródłaLarsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.
Pełny tekst źródłaSakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.
Pełny tekst źródłaThis thesis presents contributions to the problem of learning from logged interactions using the offline contextual bandit framework. We are interested in two related topics: (1) offline policy learning with performance certificates, and (2) fast and efficient policy learning applied to large scale, real world recommendation. For (1), we first leverage results from the distributionally robust optimisation framework to construct asymptotic, variance-sensitive bounds to evaluate policies' performances. These bounds lead to new, more practical learning objectives thanks to their composite nature and straightforward calibration. We then analyse the problem from the PAC-Bayesian perspective, and provide tighter, non-asymptotic bounds on the performance of policies. Our results motivate new strategies, that offer performance certificates before deploying the policies online. The newly derived strategies rely on composite learning objectives that do not require additional tuning. For (2), we first propose a hierarchical Bayesian model, that combines different signals, to efficiently estimate the quality of recommendation. We provide proper computational tools to scale the inference to real world problems, and demonstrate empirically the benefits of the approach in multiple scenarios. We then address the question of accelerating common policy optimisation approaches, particularly focusing on recommendation problems with catalogues of millions of items. We derive optimisation routines, based on new gradient approximations, computed in logarithmic time with respect to the catalogue size. Our approach improves on common, linear time gradient computations, yielding fast optimisation with no loss on the quality of the learned policies
Safran, Mejdl Sultan. "EFFICIENT LEARNING-BASED RECOMMENDATION ALGORITHMS FOR TOP-N TASKS AND TOP-N WORKERS IN LARGE-SCALE CROWDSOURCING SYSTEMS". OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1511.
Pełny tekst źródłaYang, Dingqi. "Understanding human dynamics from large-scale location-centric social media data : analysis and applications". Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0002/document.
Pełny tekst źródłaHuman dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures
Yang, Dingqi. "Understanding human dynamics from large-scale location-centric social media data : analysis and applications". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0002.
Pełny tekst źródłaHuman dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures
Östlin, Erik. "On Radio Wave Propagation Measurements and Modelling for Cellular Mobile Radio Networks". Doctoral thesis, Karlskrona : Blekinge Institute of Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00443.
Pełny tekst źródłaArrascue, Ayala Victor Anthony [Verfasser], i Georg [Akademischer Betreuer] Lausen. "Towards an effective consumption of large-scale knowledge graphs for recommendations". Freiburg : Universität, 2020. http://d-nb.info/1223366189/34.
Pełny tekst źródłaArdekani, Kamyar. "Feature Recommender : a large-scale in-situ study of proactive software feature recommendations". Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59761.
Pełny tekst źródłaScience, Faculty of
Computer Science, Department of
Graduate
Richardson, James Rutherford. "Accommodating existing settlements in large scale development : recommendations for Sha Tin New Town Hong Kong". Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/69533.
Pełny tekst źródłaThesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture; and, (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1981.
Bibliography: leaves 173-176.
by James Rutherford Richardson, IV.
Tavares, Antonio Vanderlei. "Avaliação de larga escala: resultados e tomada de decisão". Pontifícia Universidade Católica de São Paulo, 2012. https://tede2.pucsp.br/handle/handle/16037.
Pełny tekst źródłaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
This study aimed to identify and analyze recommendations and decisions from the large-scale assessment of a system of private education in the state of Sao Paulo. The methodological trajectory, initially, there were readings of the final reports of large-scale assessments to identify and document the recommendations and decisions taken in the period 1999 to 2008. Then, set up categories for the analysis of recommendations and decisions. These categories were based on the assumptions that conceive of educational assessment as a tool for monitoring and improving the quality of education offered in school systems. These assumptions were based on the principles of democratic and participative management. The analysis of the recommendations of evaluations allowed us to observe that there is a group of recommendations on teachers' work and one that is intended to administration. However, both contain recommendations that call for analysis and planning by the management team of the education system. In respect of decisions shows that there was a greater concern with the development of actions to curriculum and continuing education of educators. No actions have been identified related to contextual variables raised by the assessment scale. Both the analysis of the recommendations described in assessment reports, when the decisions indicated that the evaluation of large scale offers significant contributions on the education system, but they require planning actions intermediate between the schools and central management
Este trabalho teve o objetivo de identificar e analisar recomendações e decisões tomadas a partir da avaliação de larga escala de um sistema de ensino particular do estado de São Paulo. Na trajetória metodológica, inicialmente, foram realizadas leituras dos relatórios finais das avaliações de larga escala e de documentos para identificarmos as recomendações e as decisões tomadas no período de 1999 a 2008. Em seguida, definiram-se categorias para a análise das recomendações e decisões tomadas. Essas categorias foram elaboradas com base nos pressupostos que concebem a avaliação educacional como um instrumento para monitoramento e aprimoramento da qualidade da educação oferecida nos sistemas de ensino. Tais pressupostos foram fundamentados nos princípios da gestão democrática e participativa. As análises das recomendações das avaliações nos permitiram observar que há um grupo de recomendações sobre o trabalho docente e outro que se destina à gestão. No entanto, ambos contêm recomendações que suscitam análise e planejamento por parte da equipe gestora do sistema educativo. Quanto às decisões tomadas, observamos que houve uma preocupação maior com o desenvolvimento de ações voltadas ao currículo e à formação continuada dos educadores. Não foram identificadas ações relacionadas com variáveis contextuais levantadas pela avaliação de larga escala. Tanto as análises das recomendações descritas nos relatórios das avaliações, quando das decisões tomadas indicaram que a avaliação de larga escala oferece contribuições relevantes sobre o sistema de ensino, porém estas necessitam de ações de planejamento intermediárias entre as escolas e a gestão central
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.
Pełny tekst źródłaKun-FaLin i 林崑發. "Exploiting an Incremental SVD-based Scheme for Large-scale Recommendation Systems". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/59897143168919026531.
Pełny tekst źródłaChien, John C. "Apply Feature Extraction, SVM and LSA to Analyze Large-Scale Data for Recommendation Systems". 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0020-1308200813355700.
Pełny tekst źródłaChien, John C., i 簡健宇. "Apply Feature Extraction, SVM and LSA to Analyze Large-Scale Data for Recommendation Systems". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/51561654694589402620.
Pełny tekst źródła國立暨南國際大學
資訊工程學系
96
Recommendation systems based on collaborative filtering predict customer preference for items by learning the customer-item pairs of the past. A predominant approach to collaborative filtering is neighborhood based (KNN-based), where the customer-item preference rating is discovered from ratings of similar items or customers. In this research work, we apply latent semantic analysis of IR to discover relation densities between customers and items. Unlike previous approaches, this method does not require system to perform exhaustive search for association rules nor need to build a predefine knowledge base. This analysis is done by pure mathematic method. During the math procedures, users will be able to decide the size of data to be analyzed depending on their actual circumstances. The preliminary experiment results show an encouraging accuracy over 90%. To further deal with large-scale, sparse data, we apply SVM and feature selection techniques to reduce the dimension of data representation. SVM allows categorizing data samples into several subsets, in which the data samples are smaller. We apply feature selection in those smaller data samples to filter out less important or less relevant information that help fit the RAM size on ordinary PCs.
Fuller, Amanda Beth. "Large-scale prairie restoration four case studies and recommendations for the Badger Army Ammunition Plant /". 2002. http://catalog.hathitrust.org/api/volumes/oclc/50083823.html.
Pełny tekst źródłaTypescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (p. 159-171).
Maas, Bea. "Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yield". Doctoral thesis, 2013. http://hdl.handle.net/11858/00-1735-0000-0022-5E77-5.
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