1. SISN
  2. All Volumes and Issues
  3. Volume 11, 2022
  4. Recommendation System for Planning Leisure in Quarantine Conditions

Recommendation System for Planning Leisure in Quarantine Conditions

SISN.
2022;
Volume 11
: pp. 127 - 144
https://doi.org/10.23939/sisn2022.11.127
Authors:
  1. Oleh Veres
  2. Yana Levus
1
Lviv Polytechnic National University, Information Systems and Network
2
Lviv Polytechnic National University, Ukraine

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 psycho- emotional 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.

quarantine
methods of recommendations
method of indicator of personality types
psycho-emotional state of a person
recommendation system
  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. https://doi.org/10.1007/978-1-4899-7687-1_964
  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
  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. https://doi.org/10.3390/healthcare9010101
  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 https://doi.org/10.1016/S0140-6736(20)30460-8
  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. https://doi.org/10.1007/s12098-020-03347-3
  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. https://doi.org/10.1136/gpsych-2020-100213
  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
  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-reveals-worrying-trends
  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. https://doi.org/10.31234/osf.io/uq4rn
  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
  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-research-achievements-innovations-and-development-prospects-21-23-noyabrya-2021-goda-berlin-germaniya-arhiv/
  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. https://doi.org/10.3389/feduc.2020.572612
  13. Falk, K. (2019). Practical recommender systems. Simon and Schuster.
  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. https://doi.org/10.1088/1742-6596/1000/1/012101
  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. https://doi.org/10.1145/2843948
  16. How the YouTube algorithm works. Retrieved from https://blog.hootsuite.com/how-the-youtube-algorithm-works
  17. Katrenko, A. V. (2003). System analysis of objects and processes of computerization. Lviv: New World.
  18. Saaty, T. L. (1990). Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS publications.
  19. Saaty, T. L., & Shang, J. S. (2011). An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts. European Journal of Operational Research, 214(3), 703-715. https://doi.org/10.1016/j.ejor.2011.05.019
  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 https://doi.org/10.1090/noti944
  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. https://doi.org/10.1111/bmsp.12144
  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
  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. https://doi.org/10.3390/app10217748
  24. Jalili, M., Ahmadian, S., Izadi, M., Moradi, P., & Salehi, M. (2018). Evaluating collaborative filtering recommender algorithms: a survey. IEEE access, 6, 74003-74024. https://doi.org/10.1109/ACCESS.2018.2883742
  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 https://doi.org/10.1007/978-3-540-72079-9_12
  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. https://doi.org/10.3390/electronics11010141
  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/   https://doi.org/10.3991/ijet.v16i03.18851
  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. https://doi.org/10.3390/a13080176
  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. https://doi.org/10.1109/ICIS.2014.6912169
  30. Johnson, J. (2007). GUI bloopers 2.0: common user interface design don'ts and dos. Elsevier.
  31. Parush, A. (2015). Conceptual design for interactive systems: designing for performance and user experience. Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-419969-9.00012-7
  32. de Schipper, E., Feskens, R., & Keuning, J. (2021, March). Personalized and Automated Feedback in Summative Assessment Using Recommender Systems. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.652070
  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. https://doi.org/10.1109/STC-CSIT.2019.8929861
  34. OMG® Unified Modeling Language® (OMG UML®). Retrieved from https://www.omg.org/spec/ UML/2.5.1/PDF