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Personalized e-course implementation in university environment

REFERENCES Bakhouyi, A., Dehbi, R., Talea, M., & Batouta, Z. I. (2016). Selection of Commercial and Open Source LMS: Multi-Criteria Analysis and Advanced Comparative Study,” International Journal of Applied Engineering Research, Vol. 11, Issue 7, Delhi, India, p. 4980-4989 Bieliková, M., & Návrat, P., (2006). Modelovanie adaptívnych webových systémov. Štúdie vybraných tém softvérového inžinierstva, STU, Bratislava, s. 207-232 Bradáč, V., Šimík, M., Kotyrba, M., & Volná, E. (2017). Personalisation of a moodle course from Student’s perspective

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Personalized Information Management by Online Stores in 4C Model. Case Study

7 References [1] Andruszkiewicz, K., Betcher T., 2015. Using big data to customize the offer for customers. International Business and Global Economy , No 34/2015, pp.91–92. [2] Boulding, K.E., 1999. The Economics of Knowledge and the Knowledge of Economics. American Economic Review , Vol. 56, No. 2, pp.1-13. [3] Brusilovski, P., Kobsa, A., Nejdl, W. (ed.), 2007. The Adaptive Web: Methods and Strategies of Web Personalization . Berlin: Springer Science & Business Media. [4] Czekaj, J., 2000. Zarządzanie informacją, jako funkcja

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The Personalisation of Politics at the Local Level in Poland and Selected Central and Eastern European States: A Contribution to the Research

, Oxford. Bukowski, M. – Flis, J. – Hess, A. – Szymańska, A. (2011): Opcja czy osoba? Upartyjnienie versus personalizacja w wyborach samorządowych , Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków. Caprara, G. V. – Zimbardo P. G. (2004): Personalizing Politics. A Congruency Model of Political Preference. American Psychologist 59 (7). Deegan-Krause, K. (2010): Nowe wymiary rozłamu politycznego, in Dalton, R. – Klingemann, H. D., eds., Zachowania polityczne t. 2 , Wydawnictwo Naukowe PWN, Warszawa. Dobek-Ostrowska, B. (2004): Media masowe i

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A multi-agent brokerage platform for media content recommendation

-Redondo, R.P., Fernádez-Vilas, A. and Pazos-Arias, J.J. (2010). T-learning 2.0: A personalised hybrid approach based on ontologies and folksonomies, in F. Xhafa et al. (Eds.), Computational Intelligence for Technology Enhanced Learning, Berlin/Heidelberg, Springer, pp. 125-142. Rosaci, D. and Sarnè, G. (2013). Multi-agent technology and ontologies to support personalization in B2C e-commerce, Electronic Commerce Research and Applications 13(1): 13-23, DOI: 10.1016/j.elerap.2013.07.003. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2002

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Personality Filters for Online News Interest and Engagement

Practice , 40(5): 532-538. Flaxman, S., Goel, S. & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly , 80: 298-320. Grassegger, H. & Krogerus, M. (2017, January 28). The data that turned the world upside down. Motherboard [online]. Retrieved from https://motherboard.vice.com/en_us/article/mg9vvn/how-our-likes-helped-trump-win [accessed 2018, March 15]. Hannak, A., Sapiezynski, P., Molavi Kakhki, A., Krishnamurthy, B., Lazer, D., Mislove, A. & Wilson, C. (2013). Measuring personalization of web

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Categorization of Museum Visitors as Part of System for Personalized Museum Tour

Abstract

In the past few years, the process of lifelong learning has become more important. A tour of an educational exhibition is an interesting and attractive activity for a person receiving an education. A museum, art gallery, zoological or botanical garden or even a technological park can all be perceived as an educational exhibition. If we want the exhibition tour to provide an educational benefit to the visitor, we need to offer him adequate information about individual exhibits. The exhibition has to be personalized, that is, tailored for the various kinds of visitors. This paper deals with the issue of categorizing museum visitors using ICT, specifically an expert system which is a part of a “virtual guide”. Based on an initial analysis of a visitor, the virtual guide proposes a tour through the exhibition so that it brings the visitor the maximum educational benefit while at the same time offers information about the displayed exhibits in such a way that is most interesting and comprehensible.

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Tailoring a Fashionable Self: Sartorial Practices in an Emerging Market Context

Customization to Mass Personalization: A Strategic Transformation.’ International Journal of Flexible Manufacturing Systems 19: 533-547. Marx, Karl. 1992. Capital: Volume 1: A Critique of Political Economy. New York, NY: Penguin. Parsons, Talcott. 1951. The Social System. Glencoe, IL: The Free Press. Roman, Denise. 2007. Fragmented Identities: Popular Culture, Sex, and Everyday Life in Postcommunist Romania. Lanham: Lexington Books. Simmel, Georg. 1950. The Sociology of Georg Simmel. Edited by Kurt H. Wolff

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Personalised Hiking Time Estimation

Abstract

There are numerous attempts to estimate hiking time since the age of the ancient Roman Empire, the new digital era calls for more precise and exact solutions to be implemented in mobile applications. The importance of the topic lies in the fact that route planning algorithms and shortest path problems apply time estimations as cost functions. Our intention is to design a hiking time estimation method that accounts for terrain circumstances as well as personal factors, while the level of accuracy and the simplicity of the algorithm should enable the solution to be utilised in the practice. We refine Tobler’s earlier results to estimate a relation between terrain steepness and hiker’s velocity. Later we use fitted curve to design our novel, personalised hiking time estimation method.

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A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

References 1. Ricci, F. Travel Recommender Systems. – IEEE Intelligent Systems, Vol. 17 , 2002, No 6, pp. 55-57. 2. Abowd, G. D., C. G. Atkeson, J. Hong et al. Cyberguide: A Mobile Context-Aware Tour Guide. – Wireless Networks, Vol. 3 , 1997, No 5, pp. 421-433. 3. Horozov, T., N. Narasimhan, V. Vasudevan. Using Location for Personalized POI Recommendations in Mobile Environments. – Applications and the Internet, 2006, pp. 124-129. 4. Kim, J., H. Kim., J. Ryu. TripTip: A Trip Planning Service with Tag-Based Recommendation. – CHI’09

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Precision Medicine and its Role in the Treatment of Sepsis: A Personalised View

Introduction Hippocrates, the “Father” of modern medicine, was convinced that disease was a result of multiple factors, including the patient’s lifestyle, environmental forces and diet. Accordingly, as these are unique for every individual, treatment should be focused on the individual. He stated that because the organism acts as one, it should be treated as one, and not as individual parts of a bigger system. In recent times, a new form of medicine has become increasingly exercised, namely personalised medicine (PM) and is efficacious in many medical

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