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Статті в журналах з теми "Content recommendations"
Lidén, Erik R. "Swedish Stock Recommendations: Information Content or Price Pressure?" Multinational Finance Journal 11, no. 3/4 (December 1, 2007): 253–85. http://dx.doi.org/10.17578/11-3/4-4.
Повний текст джерелаHan, Jonghyun, Hedda R. Schmidtke, Xing Xie, and Woontack Woo. "Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity." Pervasive and Mobile Computing 13 (August 2014): 85–98. http://dx.doi.org/10.1016/j.pmcj.2013.11.002.
Повний текст джерелаYoo, Youngtae, and Hyunjun Park. "The Informational Content Of Changes In Stock Recommendation: Chaebol Vs. Non-Chaebol Affiliated Analysts." Journal of Applied Business Research (JABR) 32, no. 6 (November 2, 2016): 1687. http://dx.doi.org/10.19030/jabr.v32i6.9816.
Повний текст джерелаJabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.
Повний текст джерелаVarada, Sowmya, Ronilda Lacson, Ali S. Raja, Ivan K. Ip, Louise Schneider, David Osterbur, Paul Bain, et al. "Characteristics of knowledge content in a curated online evidence library." Journal of the American Medical Informatics Association 25, no. 5 (October 27, 2017): 507–14. http://dx.doi.org/10.1093/jamia/ocx092.
Повний текст джерелаAfolabi, Ibukun Tolulope, Opeyemi Samuel Makinde, and Olufunke Oyejoke Oladipupo. "Semantic Web mining for Content-Based Online Shopping Recommender Systems." International Journal of Intelligent Information Technologies 15, no. 4 (October 2019): 41–56. http://dx.doi.org/10.4018/ijiit.2019100103.
Повний текст джерелаJaved, Umair, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. "A Review of Content-Based and Context-Based Recommendation Systems." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (February 12, 2021): 274. http://dx.doi.org/10.3991/ijet.v16i03.18851.
Повний текст джерелаSivanaiah, Rajalakshmi, R. Sakaya Milton, and T. T. Mirnalinee. "Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1477–96. http://dx.doi.org/10.3233/ida-205244.
Повний текст джерелаAloudat, M., A. Papp, N. Magyar, L. Simon Sarkadi, and A. Lugasi. "Nutritional Value of Traditional and Modern Meals: Jordan and Hungary." Acta Alimentaria 49, no. 4 (November 7, 2020): 491–97. http://dx.doi.org/10.1556/066.2020.49.4.15.
Повний текст джерелаZadro, Joshua, Aimie L. Peek, Rachael H. Dodd, Kirsten McCaffery, and Christopher Maher. "Physiotherapists’ views on the Australian Physiotherapy Association’s Choosing Wisely recommendations: a content analysis." BMJ Open 9, no. 9 (September 2019): e031360. http://dx.doi.org/10.1136/bmjopen-2019-031360.
Повний текст джерелаДисертації з теми "Content recommendations"
Chowdhury, Mohammad Noor Nawaz. "IntelWiki - Recommending Reference Materials in Context to Facilitate Editing Wikipedia." Springer, 2014. http://hdl.handle.net/1993/23592.
Повний текст джерелаDias, Pedro Ricardo Gomes. "Recommending media content based on machine learning methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6581.
Повний текст джерелаInformation is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due to their utility and commercial potential, recommender systems integrate many audiovisual content providers and represent one of their most important and valuable services. The goal of this thesis is to develop a recommender system based on matrix factorization methods, capable of providing meaningful and personalized product recommendations to individual users and groups of users, by taking into account users’ rating patterns and biased tendencies, as well as their fluctuations throughout time.
Maes, Pauline. "Engaging Content Experience- Utilizing the Strossle recommendation capabilities, across publishers’ websites." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21487.
Повний текст джерелаBelin, Kirsten, and Yi Hsin Wang. "Job Adverts á la 2010 : A study of content, style, recommendations and students thoughts and perceptions." Thesis, Örebro universitet, Akademin för humaniora, utbildning och samhällsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-12149.
Повний текст джерелаAndersson, Morgan. "Personal news video recommendations based on implicit feedback : An evaluation of different recommender systems with sparse data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234137.
Повний текст джерелаMängden video som finns tillgänglig på internet förväntas att tredubblas år 2021 jämfört med 2016. Detta innebär ett behov av sofistikerade filter för att kunna hantera detta informationsflöde. Detta examensarbete ämnar att svara på till vilken grad det går att generera personliga rekommendationer baserat på det data som nyhetsvideo innebär. Syftet är att utvärdera och jämföra olika rekommendationssystem och hur de står sig i ett användartest. Studien utfördes under våren 2018 och utvärderar fyra olika algoritmer. Dessa olika rekommendationssystem innefattar tekniker som content-based, collaborative-filter, hybrid och en popularitetsmodell används som basvärde. Det dataset som används är glest och har endast implicita attribut. Tre experiment utförs samt ett användartest. Mätpunkten för algoritmernas prestanda utgjordes av recall at 5 och recall at 10, dvs. att man mäter hur väl algoritmerna lyckas generera värdefulla rekommendationer i en topp-fem respektive topp-10-lista av videoklipp. Detta då det är av intresse att ha de mest relevanta videorna högst upp i sin lista av resultat. En jämförelse gjordes mellan olika mängd metadata som inkluderades vid träning. Ett annat test gick ut på att utforska hur algoritmerna presterar då datasetet blir mindre glest. I användartestet användes en utvärderingsmetod kallad mean-opinion-score och denna räknades ut per algoritm genom att testanvändare gav betyg på respektive rekommendation, baserat på hur intressant videon var för dem. Användartestet inkluderade även slumpmässigt generade videos för att kunna jämföras i form av basvärde. Resultaten indikerar, för detta dataset, att algoritmen content-based presterar bäst både med hänsyn till recall at 5 & 10 samt den totala poängen i användartestet. Alla algoritmer presterade bättre än slumpen.
Дячук, Іван Сергійович. "Інтелектуальна система підбору клієнтського контенту". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/25528.
Повний текст джерелаThe master’s thesis contains the results of the development of intellectual system of selection of client content that can be used as a basis for the implementation of similar solutions. In the work the combined mathematical model and software complex with its use are developed. The results of the work were used in the development of system being put into operation, confirming the practical value of the results that were obtained.
Магистерская диссертация содержит результаты разработки интеллектуальной системы подбора клиентского контента, которые могут быть использованы, как основа для реализации аналогичных решений. В работе разработана комбинированная математическая модель и программный комплекс с ее использованием. Результаты работы были использованы при разработке системы, внедренной в эксплуатацию, что подтверждает практическое значение полученных результатов.
Angelovska, Marina. "Content-based Recommender System for Detecting Complementary Products : Evaluating Siamese Neural Networks for Predicting Complementary Relationships among E-Commerce Products." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280455.
Повний текст джерелаSå mycket som det mångfaldiga och rika utbudet på e-handelswebbplatser hjälper användarna att hitta det de behöver på en marknadsplats, är online- katalogerna ibland för överväldigande. Rekommendationssystem en viktig roll på e-handelswebbplatser eftersom de förbättrar kundupplevelsen genom att hjälpa användarna att hitta vad de vill ha i rätt ögonblick. Dessa rekommen- dationer kan baseras på användarens egenskaper, demografi, inköps- eller ses- sionshistorik.I denna avhandling fokuserar vi på att identifiera komplementära förhållanden mellan produkter för det största e-handelsföretaget i Nederländerna. Komplet- terande produkter är produkter passar väl ihop, produkter som kan vara en nödvändighet för den valda produkten eller helt enkelt ett trevligt tillskott till den. På företaget finns det stor potential eftersom kompletterande produkter ökar det genomsnittliga inköpsvärdet och de tillhandahålls för mindre än 20% av hela katalogen.Vi föreslår ett innehållsbaserat rekommendationssystem för att upptäcka kom- pletterande produkter, med en övervakad strategi för inlärning som bygger på Siamese Neural Network (SNN). Syftet med denna avhandling är i tre steg; För det första är huvudmålet att skapa en SNN-modell som kan förutsäga komplet- terande produkter för en given produkt baserat på innehållet. För detta ändamål implementerar och jämför vi två olika modeller: Siamese Convolutional Neu- ral Network och Siamese Long Short-Term Memory (LSTM) Recurrent Neural Network. Vi matar in data i dessa neurala nätverk med par produkter hämta- de från företaget, som antingen är komplementära eller icke-komplementära. Det andra grundläggande antagandet av vår metod att de flesta av de viktiga funktionerna för en produkt ingår i dess titel, men vi genomför också expe- riment inklusive produktbeskrivningen och varumärket. Slutligen föreslår vi en utvidgning av SNN-metoden för att hantera miljoner produkter på några sekunder.∼Som ett resultat av eperimenten drar vi slutsatsen att Siamese LSTM kan för- utsäga komplementära produkter med högsta noggrannhet på 85%. Vårt antagande att titeln är det mest värdefulla attributet bekräftades. Därtill är om- vandling av vår lösning till ett K-närmaste grannproblem för att optimera den för miljontals produkter gav lovande resultat.
Codina, Busquet Victor. "Exploiting distributional semantics for content-based and context-aware recommendation." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/277574.
Повний текст джерелаDurant l'última dècada, l'ús dels sistemes de recomanació s'ha vist incrementat fins al punt que, actualment, l'èxit de molts dels serveis web més coneguts depèn en aquesta tecnologia. Els Sistemes de Recomanació ajuden als usuaris a trobar els productes o serveis que més s¿adeqüen als seus interessos i preferències. Una gran limitació dels algoritmes de recomanació actuals és el problema de "data-sparsity", que es refereix a la incapacitat d'aquests sistemes de generar recomanacions precises fins que un cert nombre de votacions d'usuari és disponible per entrenar els models de predicció. Per mitigar aquest problema i millorar així la precisió de predicció de les tècniques de recomanació que conformen l'estat de l'art, en aquesta tesi hem investigat diferents maneres d'aprofitar la semàntica distribucional dels conceptes que descriuen les entitats que conformen l'espai del problema de la recomanació, principalment, els objectes a recomanar i la informació contextual. En la semàntica distribucional s'assumeix la següent hipotesi: conceptes que coincideixen repetidament en el mateix context o ús tendeixen a estar semànticament relacionats. Concretament, en aquesta tesi hem proposat i avaluat dos algoritmes de recomanació que fan ús de la semàntica distribucional per mitigar el problem de "data-sparsity": (1) un model basat en contingut que explota les similituds distribucionals dels atributs que representen els objectes a recomanar durant el càlcul de la correspondència entre els perfils d'usuari i dels objectes; (2) un model de recomanació contextual que fa ús de les similituds distribucionals entre condicions contextuals durant la representació del context. Mitjançant una avaluació experimental exhaustiva dels models de recomanació proposats hem demostrat la seva efectivitat en situacions de falta de dades, confirmant que poden millorar la precisió d'algoritmes que conformen l'estat de l'art. Finalment, aquesta tesi presenta una llibreria pel desenvolupament i avaluació d'algoritmes de recomanació com una extensió de la llibreria de "Machine Learning" Apache Mahout, àmpliament utilitzada en el camp del Machine Learning. La nostra extensió inclou tots els algoritmes de recomanació avaluats en aquesta tesi, així com una eina per facilitar l'avaluació experimental dels algoritmes. Hem desenvolupat aquesta llibreria per facilitar a altres investigadors la reproducció dels experiments realitzats i, per tant, el progrés en el camp dels Sistemes de Recomanació.
Kanard, M. Elizabeth. "Weighing in : an analysis of the NASW's web-based content regarding theoretical issues and practice recommendations for social workers working with overweight and obese individuals : a project based upon an independent investigation /." View online, 2008. http://hdl.handle.net/10090/5903.
Повний текст джерелаGibała, Karolina, and Aleksandra Gujda. "The role of peer-created content in digital advertising : Perceptions of sponsored and non-sponsored recommendations on Instagram, its recognition as a product advertisement and its effects on the level of trustworthiness." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39761.
Повний текст джерелаКниги з теми "Content recommendations"
International Business Machines Corporation. International Technical Support Organization, ed. IBM FileNet content manager implementation best practices and recommendations. [United States?]: IBM, International Technical Support Organization, 2008.
Знайти повний текст джерелаOntario. Ministry of the Environment. Lead in Soil Committee. Review and recommendations on a lead in soil guideline. [Toronto]: The Ministry, 1987.
Знайти повний текст джерелаCarbon credits from peatland rewetting: Climate, biodiversity, land use : science, policy, implementation, and recommendations of a pilot project in Belarus. Stuttgart: Schweizerbart Science Publishers, 2011.
Знайти повний текст джерелаJohnson, Art. Carbaryl concentrations in Willapa Bay and recommendations for water quality guidelines. Olympia, Wash: Washington State Department of Ecology, Environmental Assessment Program, 2001.
Знайти повний текст джерелаNeil, James. Scottish further education development plans: A study into their style, content and methodology, with recommendations. [Edinburgh?]: [Scottish Education Department?], 1989.
Знайти повний текст джерелаRice, Patricia Ohl. The accreditation of library and information science education: A content analysis of COA recommendations, 1973-1985. Ann Arbor, Mich: University Microfilms International, 1986.
Знайти повний текст джерелаPilgrim, John D. Threatened and alien species in Vietnam: Background and recommendations for the content of the national biodiversity law. Hanoi: [BirdLife International Global Forest Policy Project], 2007.
Знайти повний текст джерелаMassachusetts. Dept. of Public Health. State Laboratory Institute. A statewide survey of lead in school drinking water: An estimate of the prevalence of elevated lead levels in drinking water and recommendations for remedial action to reduce exposure to lead : executive summary. Boston (305 South St., Jamaica Plain, MA 02130): Massachusetts Dept. of Public Health, 1988.
Знайти повний текст джерелаColloquium, International Potash Institute. Development of K-fertilizer recommendations: 22nd Colloquium of the International Potash Institute, Soligorsk, USSR, June 18-23, 1990. Bern, Switzerland: The Institute, 1990.
Знайти повний текст джерелаColloquium, International Potash Institute. Development of K-fertilizer recommendations: 22nd Colloquium of the International Potash Institute, Soligorsk, USSR, June 18-23, 1990. Bern, Switzerland: Internaitonal Potash Institute, 1990.
Знайти повний текст джерелаЧастини книг з теми "Content recommendations"
Srifi, Mehdi, Badr Ait Hammou, Ayoub Ait Lahcen, and Salma Mouline. "A Concise Survey on Content Recommendations." In Communications in Computer and Information Science, 393–405. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96292-4_31.
Повний текст джерелаLee, Keonsoo, and Yunyoung Nam. "Persuading Recommendations Using Customized Content Curation." In Lecture Notes in Electrical Engineering, 159–63. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5041-1_28.
Повний текст джерелаVahidi Ferdousi, Zahra, Dario Colazzo, and Elsa Negre. "CBPF: Leveraging Context and Content Information for Better Recommendations." In Advanced Data Mining and Applications, 381–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05090-0_32.
Повний текст джерелаGuo, Weisen, and Steven B. Kraines. "Semantic Content-Based Recommendations Using Semantic Graphs." In Advances in Experimental Medicine and Biology, 653–59. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5913-3_72.
Повний текст джерелаDietz, Linus W., Sameera Thimbiri Palage, and Wolfgang Wörndl. "Navigation by Revealing Trade-offs for Content-Based Recommendations." In Information and Communication Technologies in Tourism 2022, 149–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_14.
Повний текст джерелаDragan, Łukasz, and Anna Wróblewska. "Content-Based Recommendations in an E-Commerce Platform." In Advances in Intelligent Systems and Computing, 252–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18058-4_20.
Повний текст джерелаAlmuhaimeed, Abdullah, and Maria Fasli. "Exploiting Different Bioinformatics Resources for Enhancing Content Recommendations." In Lecture Notes in Computer Science, 558–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08245-5_50.
Повний текст джерелаIvanova, Iustina, Marina Andrić, and Francesco Ricci. "Content-Based Recommendations for Crags and Climbing Routes." In Information and Communication Technologies in Tourism 2022, 369–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94751-4_33.
Повний текст джерелаWhite, Philip J., Michael J. Bell, Ivica Djalovic, Philippe Hinsinger, and Zed Rengel. "Potassium Use Efficiency of Plants." In Improving Potassium Recommendations for Agricultural Crops, 119–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_5.
Повний текст джерелаWang, Haoran, Zhengzhong Zhou, Changcheng Xiao, and Liqing Zhang. "Content Based Image Search for Clothing Recommendations in E-Commerce." In Multimedia Data Mining and Analytics, 253–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_11.
Повний текст джерелаТези доповідей конференцій з теми "Content recommendations"
Bogaards, Niels, and Frederique Schut. "Content-based book recommendations." In RecSys '21: Fifteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460231.3474603.
Повний текст джерелаZhang, Yang, and Qiang Ma. "Citation Recommendations Considering Content and Structural Context Embedding." In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2020. http://dx.doi.org/10.1109/bigcomp48618.2020.0-109.
Повний текст джерелаGautam, Anjali, Parila Chaudhary, Kunal Sindhwani, and Punam Bedi. "CBCARS: Content boosted context-aware recommendations using tensor factorization." In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2016. http://dx.doi.org/10.1109/icacci.2016.7732028.
Повний текст джерелаNessel, Jochen, and Barbara Cimpa. "The MovieOracle - Content Based Movie Recommendations." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.236.
Повний текст джерелаStecher, Rodolfo, Gianluca Demartini, and Claudia Niederée. "Social recommendations of content and metadata." In the 10th International Conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1497308.1497329.
Повний текст джерелаWang, Yiwen, Natalia Stash, Lora Aroyo, Laura Hollink, and Guus Schreiber. "Semantic relations for content-based recommendations." In the fifth international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1597735.1597786.
Повний текст джерелаKolobov, Oleg S., Anna A. Knyazeva, Yulia V. Leonova, and Igor Yu Turchanovsky. "Personalizing digital services as exemplified by library recommendation service." In Twenty Fifth International Conference and Exhibition «LIBCOM-2021». Russian National Public Library for Science and Technology, 2022. http://dx.doi.org/10.33186/978-5-85638-247-0-2022-35-40.
Повний текст джерелаHerzog, Daniel, and Wolfgang Wörndl. "Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations." In 12th International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005763702930303.
Повний текст джерелаVeas, Eduardo, Belgin Mutlu, Cecilia di Sciascio, Gerwald Tschinkel, and Vedran Sabol. "Visual Recommendations for Scientific and Cultural Content." In International Conference on Information Visualization Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005352802560261.
Повний текст джерелаWen, Hongyi, Longqi Yang, and Deborah Estrin. "Leveraging post-click feedback for content recommendations." In RecSys '19: Thirteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3298689.3347037.
Повний текст джерелаЗвіти організацій з теми "Content recommendations"
NATIONAL RESEARCH COUNCIL WASHINGTON DC. Recommendations for Content Revision and Alternate Delivery Modes for the Human Engineering Guide to Equipment Design (HEGED). Fort Belvoir, VA: Defense Technical Information Center, March 1985. http://dx.doi.org/10.21236/ada155781.
Повний текст джерелаHaßler, Björn, and Gesine Haseloff. TVET Research in SSA: Recommendations for Thematic Priorities. Undefined, February 2022. http://dx.doi.org/10.53832/opendeved.0268.
Повний текст джерелаSilverman, D. J., M. A. Bauser, and R. D. Baird. Licensing an assured isolation facility for low-level radioactive waste. Volume 2: Recommendations on the content and review of an application. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/665893.
Повний текст джерелаMoran, B., W. Belew, G. Hammond, and L. Brenner. Recommendations to the NRC on acceptable standard format and content for the Fundamental Nuclear Material Control (FNMC) Plan required for low-enriched uranium enrichment facilities. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/5978296.
Повний текст джерелаTerrón-Caro, María Teresa, Rocio Cárdenas-Rodríguez, Fabiola Ortega-de-Mora, Kassia Aleksic, Sofia Bergano, Patience Biligha, Tiziana Chiappelli, et al. Policy Recommendations ebook. Migrations, Gender and Inclusion from an International Perspective. Voices of Immigrant Women, July 2022. http://dx.doi.org/10.46661/rio.20220727_1.
Повний текст джерелаJohnson, Mark, and John Wachen. Examining Equity in Remote Learning Plans: A Content Analysis of State Responses to COVID-19. The Learning Partnership, November 2020. http://dx.doi.org/10.51420/report.2020.2.
Повний текст джерелаChang, Allan. Disclosure Standards of Large New Zealand Companies: A content analysis study of compliance with the FMA’s corporate governance guidelines. Unitec ePress, September 2017. http://dx.doi.org/10.34074/ocds.52017.
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Повний текст джерелаMintii, I. S. Using Learning Content Management System Moodle in Kryvyi Rih State Pedagogical University educational process. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3866.
Повний текст джерелаMarienko, Maiia V., Yulia H. Nosenko, and Mariya P. Shyshkina. Personalization of learning using adaptive technologies and augmented reality. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4418.
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