Gotowa bibliografia na temat „Personalization”
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Artykuły w czasopismach na temat "Personalization"
Kaneko, Kazuki, Yusuke Kishita i Yasushi Umeda. "Conducting Personalization Design Workshops — Designing Personalization Procedures". Procedia CIRP 98 (2021): 494–99. http://dx.doi.org/10.1016/j.procir.2021.01.140.
Pełny tekst źródłaFoshee, Cecile M., i Brian C. Nelson. "Avatar Personalization". International Journal of Gaming and Computer-Mediated Simulations 6, nr 2 (kwiecień 2014): 1–14. http://dx.doi.org/10.4018/ijgcms.2014040101.
Pełny tekst źródłaAdomavicius, Gediminas, i Alexander Tuzhilin. "Personalization technologies". Communications of the ACM 48, nr 10 (październik 2005): 83–90. http://dx.doi.org/10.1145/1089107.1089109.
Pełny tekst źródłaKim, Chin-Woo. "Price Personalization". LAW RESEARCH INSTITUTE CHUNGBUK NATIONAL UNIVERSITY 13, nr 2 (31.12.2022): 43–81. http://dx.doi.org/10.34267/cbstl.2022.13.2.43.
Pełny tekst źródłaLi, Cong. "When does web-based personalization really work? The distinction between actual personalization and perceived personalization". Computers in Human Behavior 54 (styczeń 2016): 25–33. http://dx.doi.org/10.1016/j.chb.2015.07.049.
Pełny tekst źródłaSchreiber, Kristin L., i Jochen D. Muehlschlegel. "Personalization over Protocolization". Anesthesiology 134, nr 3 (19.01.2021): 363–65. http://dx.doi.org/10.1097/aln.0000000000003695.
Pełny tekst źródłaOno, Akinori. "Customization and Personalization". Japan Marketing Journal 40, nr 1 (30.06.2020): 3–5. http://dx.doi.org/10.7222/marketing.2020.030.
Pełny tekst źródłaRodrigues, Luiz, Paula T. Palomino, Armando M. Toda, Ana C. T. Klock, Wilk Oliveira, Anderson P. Avila-Santos, Isabela Gasparini i Seiji Isotani. "Personalization Improves Gamification". Proceedings of the ACM on Human-Computer Interaction 5, CHI PLAY (5.10.2021): 1–25. http://dx.doi.org/10.1145/3474714.
Pełny tekst źródłaSchneider, Hanna, Florian Lachner, Malin Eiband, Ceenu George, Purvish Shah, Chinmay Parab, Anjali Kukreja, Heinrich Hussmann i Andreas Butz. "Privacy and personalization". Interactions 25, nr 3 (23.04.2018): 52–55. http://dx.doi.org/10.1145/3197571.
Pełny tekst źródłaVolokh, Eugene. "Personalization and privacy". Communications of the ACM 43, nr 8 (sierpień 2000): 84–88. http://dx.doi.org/10.1145/345124.345155.
Pełny tekst źródłaRozprawy doktorskie na temat "Personalization"
Elbassuoni, Shady. "Adaptive personalization of web search : task sensitive approach to search personalization /". Saarbrücken : VDM Verlag Dr. Müller, 2008. http://d-nb.info/988664186/04.
Pełny tekst źródłaAlmerfors, Mattias. "Visualization of Personalization Information". Thesis, Linköpings universitet, Institutionen för teknik och naturvetenskap, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97829.
Pełny tekst źródłaAsif, Muhammad. "Personalization of Mobile Services". Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25576.
Pełny tekst źródłaDonnelly, Christopher. "Enhancing Personalization Within ASSISTments". Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/249.
Pełny tekst źródłaYang, Yanwu. "Towards spatial web personalization". Paris, ENSAM, 2006. https://pastel.archives-ouvertes.fr/pastel-00002481.
Pełny tekst źródłaIn the past few years, spatial information and services have proliferated on the Web, due to the fact that most of our daily activities are related to the spatial dimension. The user communities involved in spatial web services are essentially diverse, still in an expansion and transformation with constantly increasing number of user and applications. This opens many research challenges, such as the elicitation of user's interests and preferences and customization of information services on the spatial Web. This PhD research proposes an integrated framework for user modeling and preference elicitation, and personalization services on the spatial Web. The framework identifies personalization services and a semantic user model for spatial web applications. These two components communicate information and knowledge about the user through inter-process communications. The personalization services are based on three mechanisms: the Bi-directional Neural Associative Memory, user-centric spatial proximity and similarity measures, image schemata and affordance concepts. A web-based user interface is integrated with these components, and offers a spectrum of personalized search strategies and a hybrid personalization engine. The user model employs expressive description logics to describe assumptions about the user and to infer implicit user features from user's descriptions as required by an application system. An application scenario in the tourism domain and a Web-based Java prototype provide an experimental validation of the research framework and identified personalization techniques
Boutet, Antoine. "Decentralizing news personalization systems". Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S023/document.
Pełny tekst źródłaThe rapid evolution of the web has changed the way information is created, distributed, evaluated and consumed. Users are now at the center of the web and becoming the most prolific content generators. To effectively navigate through the stream of available news, users require tools to efficiently filter the content according to their interests. To receive personalized content, users exploit social networks and recommendation systems using their private data. However, these systems face scalability issues, have difficulties in coping with interest dynamics, and raise a multitude of privacy challenges. In this thesis, we exploit peer-to-peer networks to propose a recommendation system to disseminate news in a personalized manner. Peer-to-peer approaches provide highly-scalable systems and are an interesting alternative to Big brother type companies. However, the absence of any global knowledge calls for collaborative filtering schemes that can cope with partial and dynamic interest profiles. Furthermore, the collaborative filtering schemes must not hurt the privacy of users. The first contribution of this thesis conveys the feasibility of a fully decentralized news recommender. The proposed system constructs an implicit social network based on user profiles that express the opinions of users about the news items they receive. News items are disseminated through a heterogeneous gossip protocol that (1) biases the orientation of the dissemination, and (2) amplifies dissemination based on the level of interest in each news item. Then, we propose obfuscation mechanisms to preserve privacy without sacrificing the quality of the recommendation. Finally, we explore a novel scheme leveraging the power of the distribution in a centralized architecture. This hybrid and generic scheme democratizes personalized systems by providing an online, cost-effective and scalable architecture for content providers at a minimal investment cost
Hoang, Van Tieng. "Measuring Web Search Personalization". Thesis, IMT Alti Studi Lucca, 2018. http://e-theses.imtlucca.it/246/1/Hoang_phdthesis.pdf.
Pełny tekst źródłaSONG, Songbo. "Advanced personalization of IPTV services". Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00814620.
Pełny tekst źródłaSong, Xiang Ph D. Massachusetts Institute of Technology. "Personalization of future urban mobility". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120637.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 91-97).
In the past few years, we have been experiencing rapid growth of new mobility solutions fueled by a myriad of innovations in technologies such as automated vehicles and in business models such as shared-ride services. The emerging mobility solutions are often required to be profitable, sustainable, and efficient while serving heterogeneous needs of mobility consumers. Given high-resolution consumer mobility behavior collected from smartphones and other GPS-enabled devices, the operational management strategies for future urban mobility can be personalized and serve for various system objectives. This thesis focuses on the personalization of future urban mobility through the personalized menu optimization model. The model built upon individual consumer's choice behavior generates a personalized menu for app-based mobility solutions. It integrates behavioral modeling of consumer mobility choice with optimization objectives. Individual choice behavior is modeled through logit mixture and the parameters are estimated with a hierarchical Bayes (HB) procedure. In this thesis, we first present an enhancement to HB procedure with alternative priors for covariance matrix estimation in order to improve the estimation performance. We also evaluate the benefits of personalization through a Boston case study based on real travel survey data. In addition, we present a sequential personalized menu optimization algorithm that addresses trade-off between exploration (learn uncertain demand of menus) and exploitation (offer the best menu based on current knowledge). We illustrate the benefits of exploration under different conditions including different types of heterogeneity.
by Xiang Song.
Ph. D. in Transportation
Li, Andrew A. (Andrew Andi). "Algorithms for large-scale personalization". Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119351.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 191-205).
The term personalization typically refers to the activity of online recommender systems, and while product and content personalization is now ubiquitous in e-commerce, systems today remain relatively primitive: they are built on a small fraction of available data, run with heuristic algorithms, and restricted to e-commerce applications. This thesis addresses key challenges and new applications for modern, large-scale personalization. In particular, this thesis is outlined as follows: First, we formulate a generic, flexible framework for learning from matrix-valued data, including the kinds of data commonly collected in e-commerce. Underlying this framework is a classic de-noising problem called tensor recovery, for which we provide an efficient algorithm, called Slice Learning, that is practical for massive datasets. Further, we establish near-optimal recovery guarantees that represent an order improvement over the best available results for this problem. Experimental results from a music recommendation platform are shown. Second, we apply this de-noising framework to new applications in precision medicine where data are routinely complex and in high dimensions. We describe a simple, accurate proteomic blood test (a 'liquid biopsy') for cancer detection that relies on de-noising via the Slice Learning algorithm. Experiments on plasma from healthy patients that were later diagnosed with cancer demonstrate that our test achieves diagnostically significant sensitivities and specificities for many types of cancers in their earliest stages. Third, we present an efficient, principled approach to operationalizing recommendations, i.e. the decision of exactly what items to recommend. Motivated by settings such as online advertising where the space of items is massive and recommendations must be made in milliseconds, we propose an algorithm that simultaneously achieves two important properties: (1) sublinear runtime and (2) a constant-factor guarantee under a wide class of choice models. Our algorithm relies on a new sublinear time sampling scheme, which we develop to solve a class of problems that subsumes the classic nearest neighbor problem. Results from a massive online content recommendation firm are given. Fourth, we address the problem of cost-effectively executing a broad class of computations on commercial cloud computing platforms, including the computations typically done in personalization. We formulate this as a resource allocation problem and introduce a new approach to modeling uncertainty - the Data-Driven Prophet Model - that treads the line between stochastic and adversarial modeling, and is amenable to the common situation where stochastic modeling is challenging, despite the availability of copious historical data. We propose a simple, scalable algorithm that is shown to be order-optimal in this setting. Results from experiments on a commercial cloud platform are shown.
by Andrew A. Li.
Ph. D.
Książki na temat "Personalization"
Mandai, Masaki, red. Personalization in Gynecologic Oncology. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4711-7.
Pełny tekst źródłaCarberry, Sandra, Stephan Weibelzahl, Alessandro Micarelli i Giovanni Semeraro, red. User Modeling, Adaptation, and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38844-6.
Pełny tekst źródłaHouben, Geert-Jan, Gord McCalla, Fabio Pianesi i Massimo Zancanaro, red. User Modeling, Adaptation, and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02247-0.
Pełny tekst źródłaTanaka, Yuzuru, Nicolas Spyratos, Tetsuya Yoshida i Carlo Meghini, red. Information Search, Integration and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40140-4.
Pełny tekst źródłaFlouris, Giorgos, Dominique Laurent, Dimitris Plexousakis, Nicolas Spyratos i Yuzuru Tanaka, red. Information Search, Integration, and Personalization. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44900-1.
Pełny tekst źródłaKonstan, Joseph A., Ricardo Conejo, José L. Marzo i Nuria Oliver, red. User Modeling, Adaption and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22362-4.
Pełny tekst źródłaMasthoff, Judith, Bamshad Mobasher, Michel C. Desmarais i Roger Nkambou, red. User Modeling, Adaptation, and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31454-4.
Pełny tekst źródłaGrant, Emanuel, Dimitris Kotzinos, Dominique Laurent, Nicolas Spyratos i Yuzuru Tanaka, red. Information Search, Integration, and Personalization. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43862-7.
Pełny tekst źródłaDe Bra, Paul, Alfred Kobsa i David Chin, red. User Modeling, Adaptation, and Personalization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13470-8.
Pełny tekst źródłaRicci, Francesco, Kalina Bontcheva, Owen Conlan i Séamus Lawless, red. User Modeling, Adaptation and Personalization. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20267-9.
Pełny tekst źródłaCzęści książek na temat "Personalization"
Shekhar, Shashi, i Hui Xiong. "Personalization". W Encyclopedia of GIS, 855. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_973.
Pełny tekst źródłaFraser, Stephen R. G. "Personalization". W Real-World ASP.NET: Building a Content Management System, 57–76. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-0832-7_4.
Pełny tekst źródłaGlasby, Jon, i Helen Dickinson. "Personalization". W A–Z of Inter-Agency Working, 137–41. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-00533-5_48.
Pełny tekst źródłaLorenz, Patrick A. "Personalization". W ASP.NET 2.0 Revealed, 177–98. Berkeley, CA: Apress, 2004. http://dx.doi.org/10.1007/978-1-4302-0791-7_7.
Pełny tekst źródłaWicklund, Phil. "Experience Personalization". W Practical Sitecore 8 Configuration and Strategy, 79–105. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1236-3_3.
Pełny tekst źródłaKoutrika, Georgia. "Data Personalization". W Data-Centric Systems and Applications, 213–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20062-0_11.
Pełny tekst źródłaMylonas, Phivos, i Yannis Avrithis. "Multimedia Personalization". W Encyclopedia of Multimedia, 588–89. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78414-4_50.
Pełny tekst źródłaFensel, Dieter, Borys Omelayenko, Ying Ding, Michel Klein, Alan Flett, Ellen Schulten, Guy Botquin, Mike Brown i Gloria Dabiri. "Information Personalization". W Intelligent Information Integration in B2B Electronic Commerce, 57–63. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-5538-1_6.
Pełny tekst źródłaSchulte, Stephanie Ricker. "22. Personalization". W Digital Keywords, redaktor Benjamin Peters, 242–55. Princeton: Princeton University Press, 2016. http://dx.doi.org/10.1515/9781400880553-024.
Pełny tekst źródłaBlock, Howard, Rob Castle i David Hritz. "Personalization Services". W Creating Web Portals with BEA WebLogic, 445–79. Berkeley, CA: Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-0764-1_13.
Pełny tekst źródłaStreszczenia konferencji na temat "Personalization"
Blom, Jan. "Personalization". W CHI '00 extended abstracts. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/633292.633483.
Pełny tekst źródłaLee, Min Kyung, Junsung Kim, Jodi Forlizzi i Sara Kiesler. "Personalization revisited". W the 2015 ACM International Joint Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2750858.2807552.
Pełny tekst źródłaXiao, Wenyi, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng i Qiang Yang. "Beyond Personalization". W KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330965.
Pełny tekst źródłaRey, Stéphanie, Celia Picard, Pierre Mauriéras i Anke Brock. "Personalization totem". W IHM '18: 30e Conférence Francophone sur l'Interaction Homme-Machine. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3286689.3286715.
Pełny tekst źródłaOinas-Kukkonen, Harri. "Personalization Myopia". W Mindtrek 2018: Academic Mindtrek 2018. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3275116.3275121.
Pełny tekst źródłaMiao, Xu, Chun-Te Chu, Lijun Tang, Yitong Zhou, Joel Young i Anmol Bhasin. "Distributed Personalization". W KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2788626.
Pełny tekst źródłavan Setten, Mark, Sean M. McNee i Joseph A. Konstan. "Beyond personalization". W the 10th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1040830.1040839.
Pełny tekst źródłaKuhl, Juliane, i Dieter Krause. "Identifying Potentials of Product Personalization". W ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-88463.
Pełny tekst źródłaMajumder, Anirban, i Nisheeth Shrivastava. "Know your personalization". W the 22nd international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2488388.2488464.
Pełny tekst źródłaKobsa, Alfred, Ramnath K. Chellappa i Sarah Spiekermann. "Privacy-enhanced personalization". W CHI '06 extended abstracts. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1125451.1125749.
Pełny tekst źródłaRaporty organizacyjne na temat "Personalization"
Aldrich, Susan. Agilent's Virtuous Circle of Personalization. Boston, MA: Patricia Seybold Group, sierpień 2013. http://dx.doi.org/10.1571/cs08-08-13cc.
Pełny tekst źródłaAldrich, Susan. Peerius Recommendation and Personalization Solution Evaluation. Boston, MA: Patricia Seybold Group, czerwiec 2012. http://dx.doi.org/10.1571/pr06-21-12cc.
Pełny tekst źródłaMarienko, Maiia V., Yulia H. Nosenko i Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], listopad 2020. http://dx.doi.org/10.31812/123456789/4418.
Pełny tekst źródłaSedova, Katerina, Christine McNeill, Aurora Johnson, Aditi Joshi i Ido Wulkan. AI and the Future of Disinformation Campaigns: Part 2: A Threat Model. Center for Security and Emerging Technology, grudzień 2021. http://dx.doi.org/10.51593/2021ca011.
Pełny tekst źródłaSarofim, Samer. Developing an Effective Targeted Mobile Application to Enhance Transportation Safety and Use of Active Transportation Modes in Fresno County: The Role of Application Design & Content. Mineta Transportation Institute, lipiec 2021. http://dx.doi.org/10.31979/mti.2021.2013.
Pełny tekst źródłaOsadchyi, Viacheslav V., Hanna B. Varina, Kateryna P. Osadcha, Olha V. Kovalova, Valentyna V. Voloshyna, Oleksii V. Sysoiev i Mariya P. Shyshkina. The use of augmented reality technologies in the development of emotional intelligence of future specialists of socionomic professions under the conditions of adaptive learning. CEUR Workshop Proceedings, lipiec 2020. http://dx.doi.org/10.31812/123456789/4633.
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