Рекомендаційна система планування дозвілля в умовах карантину
dc.citation.epage | 144 | |
dc.citation.issue | 11 | |
dc.citation.journalTitle | Вісник Національного університету "Львівська політехніка". Інформаційні системи та мережі | |
dc.citation.spage | 127 | |
dc.contributor.affiliation | Національний університет “Львівська політехніка” | |
dc.contributor.affiliation | Lviv Polytechnic National University | |
dc.contributor.author | Верес, Олег | |
dc.contributor.author | Левус, Яна | |
dc.contributor.author | Veres, Oleh | |
dc.contributor.author | Levus, Yana | |
dc.coverage.placename | Львів | |
dc.coverage.placename | Lviv | |
dc.date.accessioned | 2023-08-17T06:36:03Z | |
dc.date.available | 2023-08-17T06:36:03Z | |
dc.date.created | 2022-03-01 | |
dc.date.issued | 2022-03-01 | |
dc.description.abstract | Досліджено проблему планування організації дозвілля у періоди вимушеного перебування вдома із використанням засобів інформаційних технологій. У роботі описано проблеми, які виникають у мешканців міст під час карантинних обмежень, та їх вплив на психоемоційне здоров’я людини. Визначено потребу в адаптації та модифікації звичних форм активності дозвілля до нового формату, оскільки найвідоміші сучасні інформаційні системи, які надають послуги із дозвілля, є вузькоспеціалізованими. Вони генерують рекомендації, які пов’язані з медіа-сервісами. Досліджено методи надання рекомендацій. Для вирішення проблемної ситуації побудовано дерево цілей. Розглянуто альтернативні засоби реалізації інформаційної системи. За допомогою методу аналізу ієрархії (МАІ) вибрано оптимальний тип системи для реалізації пропонованого рішення – рекомендаційну систему. Описано алгоритм роботи рекомендаційної системи, що пропонує альтернативи проведення вільного часу у періоди вимушеного перебування вдома. Використано механізм зваженого гібриду для надання рекомендацій. Для створення портрета користувача використано метод індикаторів типів особистості. За допомогою засобів мови UML спроєктовано концептуальну модель системи. Для реалізації прототипу мобільного застосунку системи вибрано мови програмування Java, JavaScript, фрейморк React Native. Для роботи із базою даних вибрано систему управління бази даних MySQL. Наведено приклад використання системи у вигляді мобільного застосунку. Описано основні етапи взаємодії користувача із рекомендаційною системою, що сприяє організації проведення вільного часу в періоди вимушеного перебування вдома. Робота рекомендаційної системи спрямована на послаблення негативних наслідків перебування у режимі вимушеного карантину на психоемоційний стан людини. Особливістю рекомендацій розробленого прототипу інформаційної системи є надання пропозицій, які містять, окрім пасивних видів проведення дозвілля, також активні, що враховують особливості кожного її користувача. Застосування рекомендаційної системи не обмежується лише карантинним чинником. Послугами рекомендаційної системи доцільно скористатись людям з обмеженими можливостями, після фізичних травм, що призвели до тимчасової малорухомості, та в період реабілітації від їх наслідків. | |
dc.description.abstract | The work is devoted to research on the problem of management and organization of free time during the period of forced stay at home by means of information technologies. The paper describes the problems during quarantine restrictions and how this affects the psychoemotional health of the person. The need to adapt and modify the usual forms of leisure activity to the new format has been determined. The most famous modern information systems, providing entertainment services are narrow-purpose systems. They generate recommendations related to media services. Methods of providing recommendations have been studied. A tree of goals was built to solve the problem situation. Alternative means of implementation of the information system are considered. Using the method of the hierarchical analysis, the optimal system type of implementation of the proposed solution is chosen – the recommendation system. The algorithm of work of the recommendation system of free time during the period of forced stay at home is described. The mechanism of weight optimization in the weighted hybrid recommendation algorithm was used to provide recommendations. When a user's portrait is created, the method of the personality type indicator is used. Using the UML language tools, a conceptual system model has been designed. For realization of the prototype of a mobile application of the system language programming Java, JavaScript, frame react Native is chosen. To work with the database the MySQL database management system has been selected. An example of using the system as a mobile application is given. The main stages of interaction of the user with the recommended system of free time during the period of forced stay at home are described. The work of the recommendation system is aimed at mitigating the negative consequences on the psycho-emotional state of a person who is in the conditions of forced quarantine. The special feature of the recommendations of the developed prototype is to offer, in addition to passive activities, active actions that take into account the peculiarities of each user. Application of the system is not limited only to quarantine. The services of the system will be appropriate for people with disabilities, in the case of physical injury transfer or liquidation, which led to temporary immobility. | |
dc.format.extent | 127-144 | |
dc.format.pages | 18 | |
dc.identifier.citation | Верес О. Рекомендаційна система планування дозвілля в умовах карантину / Олег Верес, Яна Левус // Вісник Національного університету "Львівська політехніка". Інформаційні системи та мережі. — Львів : Видавництво Львівської політехніки, 2022. — № 11. — С. 127–144. | |
dc.identifier.citationen | Veres O. Recommendation system for planning leisure in quarantine conditions / Oleh Veres, Yana Levus // Visnyk Natsionalnoho universytetu "Lvivska politekhnika". Informatsiini systemy ta merezhi. — Lviv : Lviv Politechnic Publishing House, 2022. — No 11. — P. 127–144. | |
dc.identifier.doi | doi.org/10.23939/sisn2022.11.127 | |
dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/59482 | |
dc.language.iso | uk | |
dc.publisher | Видавництво Львівської політехніки | |
dc.publisher | Lviv Politechnic Publishing House | |
dc.relation.ispartof | Вісник Національного університету "Львівська політехніка". Інформаційні системи та мережі, 11, 2022 | |
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dc.relation.references | 20. Saaty, T. L. (2013). On the measurement of intengibles. A principal eigenvector approach to relative measurement derived from paired comparisons. Notices of the American Mathematical Society, 60(2), 192–208. Retrieved from https://dialnet.unirioja.es/ejemplar/323067. | |
dc.relation.references | 21. Tang, X., Chen, Y., Li, X., Liu, J., & Ying, Z. (2019). A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology, 72(1), 108–135. DOI: 10.1111/bmsp.12144. | |
dc.relation.references | 22. Cherry, K. (2021, July). An Overview of the Myers-Briggs Type Indicator. Retrieved from https://www.verywellmind.com/the-myers-briggs-type-indicator-2795583. | |
dc.relation.references | 23. Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. applied sciences, 10(21), 7748. DOI: 10.3390/app10217748. | |
dc.relation.references | 24.Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., & Salehi, M. (2018). Evaluating collaborative filtering recommender algorithms: a survey. IEEE access, 6, 74003–74024. DOI: 10.1109/ACCESS.2018.2883742. | |
dc.relation.references | 25.Burke, R. (2007). Hybrid web recommender systems. The adaptive web, 377–408. Retrieved from https://link.springer.com/chapter/10.1007/978-3-540-72079-9_12. | |
dc.relation.references | 26. Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics, 11(1), 141. DOI: 10.3390/electronics11010141. | |
dc.relation.references | 27.Javed, U., Shaukat, K., Hameed, I. A., Iqbal, F., Alam, T. M., & Luo, S. (2021). A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning (iJET), 16(3), 274–306. Retrieved from https://www.learntechlib.org/p/219036/ | |
dc.relation.references | 28.Beheshti, A., Yakhchi, S., Mousaeirad, S., Ghafari, S. M., Goluguri, S. R., & Edrisi, M. A. (2020). Towards cognitive recommender systems. Algorithms, 13(8), 176. DOI: 10.3390/a13080176. | |
dc.relation.references | 29. Lin, W., Li, Y., Feng, S., & Wang, Y. (2014, June). The optimization of weights in weighted hybrid recommendation algorithm. In 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS) , 415–418. DOI: 10.1109/ICIS.2014.6912169. | |
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dc.relation.references | 31. Parush, A. (2015). Conceptual design for interactive systems: designing for performance and user experience. Morgan Kaufmann. | |
dc.relation.references | 32. de Schipper, E., Feskens, R., & Keuning, J. (2021, March). Personalized and Automated Feedback in Summative Assessment Using Recommender Systems. Frontiers in Education, 6. DOI: 10.3389/feduc.2021.652070. | |
dc.relation.references | 33. Veres, O., Kunanets, N., Pasichnyk, V., Veretennikova, N., Korz, R., & Leheza, A. (2019, September). Development and Operations-the Modern Paradigm of the Work of IT Project Teams. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT) , 3, 103–106. IEEE. DOI: 10.1109/STC-CSIT.2019.8929861. | |
dc.relation.references | 34. OMG® Unified Modeling Language® (OMG UML®). Retrieved from https://www.omg.org/spec/UML/2.5.1/PDF. | |
dc.relation.referencesen | 1. Melville P., Sindhwani V. (2017). Recommender Systems. In: Sammut C., Webb G. I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. DOI: 10.1007/978-1-4899-7687-1_964. | |
dc.relation.referencesen | 2. The Guardian. (2020). Mental health suffers under the lockdown. Retrieved from https://www.theguardian.com/world/2020/apr/30/mental-health-suffers-under-the-lockdown. | |
dc.relation.referencesen | 3. Cavicchioli, M., Ferrucci, R., Guidetti, M., Canevini, M. P., Pravettoni, G., & Galli, F. (2021). What will be the impact of the Covid-19 quarantine on psychological distress? Considerations based on a systematic review of pandemic outbreaks. Healthcare, 9 (1), 101. DOI: 10.3390/healthcare9010101. | |
dc.relation.referencesen | 4. Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G. J. (2020). The psychological impact of quarantine and how to reduce it: rapid review of the evidence. The lancet, 395 (10227), 912–920. DOI: 10.1016/S0140-6736(20)30460-8. | |
dc.relation.referencesen | 5. Saurabh, K., & Ranjan, S. (2020). Compliance and psychological impact of quarantine in children and adolescents due to Covid-19 pandemic. The Indian Journal of Pediatrics, 87(7), 532–536. DOI: 10.1007/s12098-020-03347-3. | |
dc.relation.referencesen | 6. Qiu, J., Shen, B., Zhao, M., Wang, Z., Xie, B., & Xu, Y. (2020). A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations. General psychiatry, 33:e100213. DOI: 10.1136/ gpsych-2020-100213. | |
dc.relation.referencesen | 7. Weissbourd, R., Batanova, M., Lovison, V., & Torres, E. (2021). Loneliness in America how the Pandemic Has Deepened an Epidemic of Loneliness and What We Can Do about it. Making Caring Common, 1–13. Retrieved from https://mcc.gse.harvard.edu/reports/loneliness-in-america. | |
dc.relation.referencesen | 8. Medical News Today. (n.d.). How do people cope with the pandemic? Survey reveals worrying trends. Retrieved from https://www.medicalnewstoday.com/articles/how-do-people-cope-with-the-pandemic-survey-revealsworrying-trends. | |
dc.relation.referencesen | 9. Levita, L., Miller, J. G., Hartman, T. K., Murphy, J., Shevlin, M., McBride, O., & Bental, R. (2021). Report1: Impact of Covid-19 on young people aged 13-24 in the UK-preliminary findings. Retrieved from https://psyarxiv.com/uq4rn. | |
dc.relation.referencesen | 10. App Usage error data report. (n.d.). Software Bugs Don’t Shelter in Place: What app usage and error data reveal during COVID-19. Retrieved from https://www.bugsnag.com/covid-19-app-usage-error-data-report. | |
dc.relation.referencesen | 11. Veres, О., & Levus, J. I. (2021). Recommendation system of time management during the period of forced stay at home. Modern scientific research: achievements, innovations and development prospects. Proceedings of the 6th International scientific and practical conference. MDPC Publishing. Berlin, Germany. 244–248. Retrieved from https://sci-conf.com.ua/vi-mezhdunarodnaya-nauchno-prakticheskaya-konferentsiya-modern-scientific-researchachievements-innovations-and-development-prospects-21-23-noyabrya-2021-goda-berlin-germaniya-arhiv/ | |
dc.relation.referencesen | 12.Bulut, O., Cormier, D. C., & Shin, J. (2020). An intelligent recommender system for personalized test administration scheduling with computerized formative assessments. Front. Educ. 5:572612. DOI: 10.3389/feduc.2020.572612. | |
dc.relation.referencesen | 13. Falk, K. (2019). Practical recommender systems. Simon and Schuster. | |
dc.relation.referencesen | 14. Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018, April). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics: Conf. Series 1000 (2018) 012101. DOI :10.1088/1742-6596/1000/1/012101. | |
dc.relation.referencesen | 15. Gomez-Uribe, C. A., & Hunt, N. (2016). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS), 6(4), 1–19. DOI: 10.1145/2843948. | |
dc.relation.referencesen | 16. How the YouTube algorithm works. Retrieved from https://blog.hootsuite.com/how-the-youtube-algorithmworks | |
dc.relation.referencesen | 17. Katrenko, A. V. (2003). System analysis of objects and processes of computerization. Lviv: New World. | |
dc.relation.referencesen | 18. Saaty, T. L. (1990). Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS publications. | |
dc.relation.referencesen | 19. Saaty, T. L., & Shang, J. S. (2011). An innovative orders-of-magnitude approach to AHP-based mutlicriteria decision making: Prioritizing divergent intangible humane acts. European Journal of Operational Research, 214(3), 703–715.DOI: 10.1016/j.ejor.2011.05.019. | |
dc.relation.referencesen | 20. Saaty, T. L. (2013). On the measurement of intengibles. A principal eigenvector approach to relative measurement derived from paired comparisons. Notices of the American Mathematical Society, 60(2), 192–208. Retrieved from https://dialnet.unirioja.es/ejemplar/323067. | |
dc.relation.referencesen | 21. Tang, X., Chen, Y., Li, X., Liu, J., & Ying, Z. (2019). A reinforcement learning approach to personalized learning recommendation systems. British Journal of Mathematical and Statistical Psychology, 72(1), 108–135. DOI: 10.1111/bmsp.12144. | |
dc.relation.referencesen | 22. Cherry, K. (2021, July). An Overview of the Myers-Briggs Type Indicator. Retrieved from https://www.verywellmind.com/the-myers-briggs-type-indicator-2795583. | |
dc.relation.referencesen | 23. Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. applied sciences, 10(21), 7748. DOI: 10.3390/app10217748. | |
dc.relation.referencesen | 24.Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., & Salehi, M. (2018). Evaluating collaborative filtering recommender algorithms: a survey. IEEE access, 6, 74003–74024. DOI: 10.1109/ACCESS.2018.2883742. | |
dc.relation.referencesen | 25.Burke, R. (2007). Hybrid web recommender systems. The adaptive web, 377–408. Retrieved from https://link.springer.com/chapter/10.1007/978-3-540-72079-9_12. | |
dc.relation.referencesen | 26. Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics, 11(1), 141. DOI: 10.3390/electronics11010141. | |
dc.relation.referencesen | 27.Javed, U., Shaukat, K., Hameed, I. A., Iqbal, F., Alam, T. M., & Luo, S. (2021). A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning (iJET), 16(3), 274–306. Retrieved from https://www.learntechlib.org/p/219036/ | |
dc.relation.referencesen | 28.Beheshti, A., Yakhchi, S., Mousaeirad, S., Ghafari, S. M., Goluguri, S. R., & Edrisi, M. A. (2020). Towards cognitive recommender systems. Algorithms, 13(8), 176. DOI: 10.3390/a13080176. | |
dc.relation.referencesen | 29. Lin, W., Li, Y., Feng, S., & Wang, Y. (2014, June). The optimization of weights in weighted hybrid recommendation algorithm. In 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS) , 415-418. DOI: 10.1109/ICIS.2014.6912169. | |
dc.relation.referencesen | 30.Johnson, J. (2007). GUI bloopers 2.0: common user interface design don’ts and dos. Elsevier. | |
dc.relation.referencesen | 31. Parush, A. (2015). Conceptual design for interactive systems: designing for performance and user experience. Morgan Kaufmann. | |
dc.relation.referencesen | 32. de Schipper, E., Feskens, R., & Keuning, J. (2021, March). Personalized and Automated Feedback in Summative Assessment Using Recommender Systems. Frontiers in Education, 6. DOI: 10.3389/feduc.2021.652070. | |
dc.relation.referencesen | 33. Veres, O., Kunanets, N., Pasichnyk, V., Veretennikova, N., Korz, R., & Leheza, A. (2019, September). Development and Operations-the Modern Paradigm of the Work of IT Project Teams. In 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT) , 3, 103–106. IEEE. DOI: 10.1109/STC-CSIT.2019.8929861. | |
dc.relation.referencesen | 34. OMG® Unified Modeling Language® (OMG UML®). Retrieved from https://www.omg.org/spec/UML/2.5.1/PDF. | |
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dc.relation.uri | https://mcc.gse.harvard.edu/reports/loneliness-in-america | |
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dc.rights.holder | © Національний університет “Львівська політехніка”, 2022 | |
dc.rights.holder | © Верес О., Левус Я., 2022 | |
dc.subject | карантин | |
dc.subject | методи рекомендацій | |
dc.subject | метод індикатора типів особистості | |
dc.subject | психоемоційний стан людини | |
dc.subject | рекомендаційна система | |
dc.subject | quarantine | |
dc.subject | methods of recommendations | |
dc.subject | method of indicator of personality types | |
dc.subject | psycho-emotional state of a person | |
dc.subject | recommendation system | |
dc.subject.udc | 004.8 | |
dc.title | Рекомендаційна система планування дозвілля в умовах карантину | |
dc.title.alternative | Recommendation system for planning leisure in quarantine conditions | |
dc.type | Article |
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