Застосування методів рекомендацій в процесах аналізу комп’ютерних комплектуючих

Simple item page
dc.citation.epage98
dc.citation.issue14
dc.citation.journalTitleВісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі
dc.citation.spage84
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorВерес, Олег
dc.contributor.authorГадзало, Олег
dc.contributor.authorVeres, Oleh
dc.contributor.authorHadzalo, Oleh
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-09-12T07:22:08Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractДосліджено проблеми аналізу комп’ютерних комплектуючих, щоб полегшити конструювання комп’ютерів, виконати повний аналіз та покращити інформаційно-технічну допомогу користувачам із використанням засобів інформаційних технологій. У роботі описано процес інформаційно-технічної допомоги користувачам з різними комп’ютерними проблемами. Визначено потребу в розробленні системи аналізу комп’ютерних комплектуючих для полегшення конструювання комп’ютерів, їх повне аналізування, створення аналітики проблеми та способів її вирішення й поліпшення інформаційно-технічної допомоги користувачам із комп’ютерними проблемами. Проаналізовано підходи до застосування методології та рішень щодо аналізу комп’ютерних комплектуючих, а також досліджено методи надання рекомендацій. Для генерування пропонованої користувачу множини комплектуючих найкраще застосовувати методи рекомендацій. Стосовно комп’ютерних комплектуючих доцільніше надавати рекомендації для груп користувачів, аніж для окремих користувачів. Для пошуку груп користувачів використано метод мішаної категоріально-чисельної кластеризації, який враховує числові рейтингові та демографічні характеристики користувачів. Використано гібридний метод пошуку груп користувачів, який ґрунтується на коефіцієнті розрідженості матриці користувач– предмет. Описано алгоритм роботи гібридної рекомендаційної системи, що пропонує комп’ютерні комплектуючі залежно від варіантів сформульованих вимог користувача. Використано механізм зваженого гібриду для надання рекомендацій. За допомогою засобів мови UML спроєктовано концептуальну модель системи. Рекомендаційна система дає змогу користувачу застосувати аналізатор власного комп’ютера, який виявить застарілі комплектуючі та запропонує якісніші деталі й, головне, які максимально підходять. Якщо ж користувач хоче абсолютно новий комп’ютер, можна скористатись конструктором збірок, який на основі рекомендаційної системи підбирає комплектуючі, які відповідають заданому запиту користувача, або ж уже вибраній частині комп’ютера.
dc.description.abstractToday, the improvement of information and technical assistance to users through information technology is relevant. To improve the design of computers, we analyze its components and study the architecture, as well as the process of improving the functionality of a computer. We conducted an analytical review of existing software solutions for analyzing computer components. We consider models for forming a set of recommendations taking into account the wishes of the user. Given the specifics of the analysis of the problem situation, it is proposed to unite users into groups. Mixed categorical-numerical clustering was used to search for user groups. This took into account the numerical (Item ratings) and demographic properties of users, as well as the sparsity coefficient of the User-Item Matrix. His algorithm of operation of the hybrid recommendation system is described, which proposes to take into account the user's requirements when analyzing and generating component variability for a computer, a hybrid model of providing recommendations with a weighted weight factor is used. UML provides a conceptual model of the system. The recommendation system allows the user to use computer analysis of components, which will offer the best components and, most importantly, the most suitable details. If the user wants a completely new computer, he can use the assembly designer. Components will be selected for the user request, or a part of the computer will be offered. The target audience of the program is PC users of any age.
dc.format.extent84-98
dc.format.pages15
dc.identifier.citationВерес О. Застосування методів рекомендацій в процесах аналізу комп’ютерних комплектуючих / Олег Верес, Олег Гадзало // Вісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі. — Львів : Видавництво Львівської політехніки, 2023. — № 14. — С. 84–98.
dc.identifier.citationenVeres O. Application of methods of recommendations in the analysis of computer components / Oleh Veres, Oleh Hadzalo // Information Systems and Networks. — Lviv : Lviv Politechnic Publishing House, 2023. — No 14. — P. 84–98.
dc.identifier.doidoi.org/10.23939/sisn2023.14.084
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/111721
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі, 14, 2023
dc.relation.ispartofInformation Systems and Networks, 14, 2023
dc.relation.references1. 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.references2. Павлиш, В. А., Гліненко, Л. К. & Шаховська, Н. Б. (2018). Основи інформаційних технологій і систем: підручник. Львів: Вид-во Львівської політехніки.
dc.relation.references3. Best benchmarks and their pluses. URL: https://www.techradar.com/best/best-benchmarks-software
dc.relation.references4. 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.references5. Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010, September). Understanding choice overload in recommender systems. In Proceedings of the fourth ACM conference on Recommender systems, 63–70.
dc.relation.references6. Верес, О., & Левус, Я. І. (2022). Рекомендаційна система планування дозвілля в умовах карантину. Вісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі, 11, 127–144. DOI: https://doi.org/10.23939/sisn2022.11.127
dc.relation.references7. Veres, O., Ilchuk, P., Kots, O., & Levus, Y. (2022, November). Information System for Leisure Time-Management in Quarantine Conditions. In 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 156–159. IEEE. Date Added to IEEE Xplore: 02 January 2023. DOI: 10.1109/CSIT56902.2022.10000478
dc.relation.references8. Veres, O., Ilchuk, P., & Kots, O. (2021). Intelligent Information System for Remote Customer Service. In MoMLeT+ DS, 283–299. https://ceur-ws.org/Vol-3312/paper22.pdf
dc.relation.references9. 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.references10. Falk, K. (2019). Practical recommender systems. Simon and Schuster.
dc.relation.references11. 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.
dc.relation.references12. Atas, M., Felfernig, A., Polat-Erdeniz, S., Popescu, A., Tran, T. N. T., & Uta, M. (2021). Towards psychology-aware preference construction in recommender systems: Overview and research issues. Journal of Intelligent Information Systems, 57, 467–489. https://doi.org/10.1007/s10844-021-00674-5.
dc.relation.references13. Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, 114324. https://doi.org/10.1016/j.eswa.2020.114324.
dc.relation.references14. Amato, F., Moscato, V., Picariello, A., & Piccialli, F. (2019). SOS: a multimedia recommender system for online social networks. Future generation computer systems, 93, 914–923. https://doi.org/10.1016/j.future.2017.04.028.
dc.relation.references15. Loeb, S., & Terry, D. (1992). Information filtering. Communications of the ACM, 35(12), 26–28. http://doi.org/10.1145/138859.138860.
dc.relation.references16. Cantador, I., Fernández, M., Vallet, D., Castells, P., Picault, J., & Ribiere, M. (2008). A multi-purpose ontology-based approach for personalised content filtering and retrieval. Advances in Semantic Media Adaptation and Personalization, 25–51. ISBN 978-3-540-76359-8.
dc.relation.references17. 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.references18. 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.references19. Salter, J., & Antonopoulos, N. (2006). CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intelligent Systems, 21(1), 35–41. http://doi.org/10.1109/MIS.2006.4
dc.relation.references20. Li, B., Li, G., Xu, J., Li, X., Liu, X., Wang, M., & Lv, J. (2023). A personalized recommendation framework based on MOOC system integrating deep learning and big data. Computers and Electrical Engineering, 106, 108571. https://doi.org/10.1016/j.compeleceng.2022.108571.
dc.relation.references21. Lathabai, H. H., Nandy, A., & Singh, V. K. (2022). Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis. Expert Systems with Applications, 209, 118317. https://doi.org/10.1016/j.eswa.2022.118317.
dc.relation.references22. Shivaram, K., Liu, P., Shapiro, M., Bilgic, M., & Culotta, A. (2022, September). Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems, 220–228. https://doi.org/10.1145/3523227.3546782.
dc.relation.references23. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70. http://doi.org/10.1145/138859.138867.
dc.relation.references24. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994, October). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. https://doi.org/10.1145/192844.192905
dc.relation.references25. Park, S. H., & Han, S. P. (2012, August). Empirical analysis of the impact of product diversity on long-term performance of recommender systems. In Proceedings of the 14th Annual International Conference on Electronic Commerce, 280–281. https://doi.org/10.1145/2346536.2346592
dc.relation.references26. Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507–520. http://doi.org/10.1016/j.eswa.2017.09.058
dc.relation.references27. Abdelwahab, A., Sekiya, H., Matsuba, I., Horiuchi, Y., & Kuroiwa, S. (2012). Alleviating the sparsity problem of collaborative filtering using an efficient iterative clustered prediction technique. International Journal of Information Technology & Decision Making, 11(01), 33–53. https://doi.org/10.1142/S0219622012500022
dc.relation.references28. Martins, G. B., Papa, J. P., & Adeli, H. (2020). Deep learning techniques for recommender systems based on collaborative filtering. Expert Systems, 37(6), e12647. https://doi.org/10.1111/exsy.12647
dc.relation.references29. Alabdulrahman, R., & Viktor, H. (2021). Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning. Expert Systems with Applications, 166, 114061. https://doi.org/10.1016/j.eswa.2020.114061.
dc.relation.references30. Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. http://doi.org/10.1509/jmkr.37.3.363.18779
dc.relation.references31. Basilico, J., & Hofmann, T. (2004, July). Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning, p. 9. https://doi.org/10.1145/1015330.1015394
dc.relation.references32. Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487–1524. DOI: 10.3233/IDA-163209.
dc.relation.references33. CPU-Z System information software. URL: https://www.cpuid.com/softwares/cpu-z.html
dc.relation.references34. GPU-Z Main settings and features. URL: https://www.techpowerup.com/gpuz/
dc.relation.references35. AIDA64 Extreme settings. URL: https://www.aida64.com/products/aida64-extreme
dc.relation.references36. Speccy main projects and opportunities. URL: https://www.ccleaner.com/speccy
dc.relation.references37. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge based systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
dc.relation.references38. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734–749. DOI: 10.1109/TKDE.2005.99
dc.relation.references39. Лобур, М. В., Шварц, М. Є., & Стех, Ю. В. (2018). Моделі і методи прогнозування рекомендацій для колаборативних рекомендаційних систем. Вісник Національного університету “Львівська політехніка”. Серія: Інформаційні системи та мережі, (901), 68–75. URL: https://science.lpnu.ua/sites/default/files/journal-paper/2019/feb/15581/181912maket-68-75.pdf
dc.relation.references40. Masthoff, J. (2004). Group modeling: Selecting a sequence of television items to suit a group of viewers. Personalized Digital Television: Targeting Programs to individual Viewers, 93–141. https://doi.org/10.1007/1-4020-2164-X_5
dc.relation.references41. Guha, S., Rastogi, R., & Shim, K. (2000). ROCK: A robust clustering algorithm for categorical attributes. Information systems, 25(5), 345–366. https://doi.org/10.1016/S0306-4379(00)00022-3.
dc.relation.references42. Burke, R. (2007). Hybrid web recommender systems. The adaptive web, 377–408. URL: https://link.springer.com/chapter/10.1007/978-3-540-72079-9_12
dc.relation.references43. 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.references44. 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. URL: https://www.learntechlib.org/p/219036/
dc.relation.references45. 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.references46. 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.references47. 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.references48. Johnson, J. (2007). GUI bloopers 2.0: common user interface design don’ts and dos. Elsevier.
dc.relation.references49. Parush, A. (2015). Conceptual design for interactive systems: designing for performance and user experience. Morgan Kaufmann.
dc.relation.references50. 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.references51. 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.references52. OMG® Unified Modeling Language® (OMG UML®). URL: https://www.omg.org/spec/UML/2.5.1/PDF
dc.relation.referencesen1. 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.referencesen2. Pavlysh, V. A., Glinenko, L. K. & Shakhovska, N. B. (2018). Fundamentals of information technologies and systems: a textbook. Lviv: Department of Lviv Polytechnic.
dc.relation.referencesen3. Best benchmarks and their pluses. URL: https://www.techradar.com/best/best-benchmarks-software
dc.relation.referencesen4. 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.referencesen5. Bollen, D., Knijnenburg, B. P., Willemsen, M. C., & Graus, M. (2010, September). Understanding choice overload in recommender systems. In Proceedings of the fourth ACM conference on Recommender systems, 63–70.
dc.relation.referencesen6. Veres, O., & Levus, Y. I. (2022). Recommendation system for leisure planning in quarantine conditions. Bulletin of the Lviv Polytechnic National University. Series: Information systems and networks. 11, 127–144. DOI: https://doi.org/10.23939/sisn2022.11.127
dc.relation.referencesen7. Veres, O., Ilchuk, P., Kots, O., & Levus, Y. (2022, November). Information System for Leisure Time Management in Quarantine Conditions. In 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT), 156–159. IEEE. Date Added to IEEE Xplore: 02 January 2023. DOI: 10.1109/CSIT56902.2022.10000478
dc.relation.referencesen8. Veres, O., Ilchuk, P., & Kots, O. (2021). Intelligent Information System for Remote Customer Service. In MoMLeT+ DS, 283–299. https://ceur-ws.org/Vol-3312/paper22.pdf
dc.relation.referencesen9. 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.referencesen10. Falk, K. (2019). Practical recommender systems. Simon and Schuster.
dc.relation.referencesen11. 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.
dc.relation.referencesen12. Atas, M., Felfernig, A., Polat-Erdeniz, S., Popescu, A., Tran, T. N. T., & Uta, M. (2021). Towards psychology-aware preference construction in recommender systems: Overview and research issues. Journal of Intelligent Information Systems, 57, 467–489. https://doi.org/10.1007/s10844-021-00674-5.
dc.relation.referencesen13. Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, 114324. https://doi.org/10.1016/j.eswa.2020.114324.
dc.relation.referencesen14. Amato, F., Moscato, V., Picariello, A., & Piccialli, F. (2019). SOS: a multimedia recommender system for online social networks. Future generation computer systems, 93, 914-923. https://doi.org/10.1016/j.future.2017.04.028.
dc.relation.referencesen15. Loeb, S., & Terry, D. (1992). Information filtering. Communications of the ACM, 35(12), 26–28. http://doi.org/10.1145/138859.138860.
dc.relation.referencesen16. Cantador, I., Fernández, M., Vallet, D., Castells, P., Picault, J., & Ribiere, M. (2008). A multi-purpose ontology-based approach for personalised content filtering and retrieval. Advances in Semantic Media Adaptation and Personalization, 25–51. ISBN 978-3-540-76359-8.
dc.relation.referencesen17. 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.referencesen18. 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.referencesen19. Salter, J., & Antonopoulos, N. (2006). CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intelligent Systems, 21(1), 35–41. http://doi.org/10.1109/MIS.2006.4
dc.relation.referencesen20. Li, B., Li, G., Xu, J., Li, X., Liu, X., Wang, M., & Lv, J. (2023). A personalized recommendation framework based on MOOC system integrating deep learning and big data. Computers and Electrical Engineering, 106, 108571. https://doi.org/10.1016/j.compeleceng.2022.108571.
dc.relation.referencesen21. Lathabai, H. H., Nandy, A., & Singh, V. K. (2022). Institutional collaboration recommendation: An expertise-based framework using NLP and network analysis. Expert Systems with Applications, 209, 118317. https://doi.org/10.1016/j.eswa.2022.118317.
dc.relation.referencesen22. Shivaram, K., Liu, P., Shapiro, M., Bilgic, M., & Culotta, A. (2022, September). Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation. In Proceedings of the 16th ACM Conference on Recommender Systems, 220–228. https://doi.org/10.1145/3523227.3546782.
dc.relation.referencesen23. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70. http://doi.org/10.1145/138859.138867.
dc.relation.referencesen24. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994, October). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, 175–186. https://doi.org/10.1145/192844.192905
dc.relation.referencesen25. Park, S. H., & Han, S. P. (2012, August). Empirical analysis of the impact of product diversity on longterm performance of recommender systems. In Proceedings of the 14th Annual International Conference on Electronic Commerce, 280–281. https://doi.org/10.1145/2346536.2346592
dc.relation.referencesen26. Nilashi, M., Ibrahim, O., & Bagherifard, K. (2018). A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Applications, 92, 507–520. http://doi.org/10.1016/j.eswa.2017.09.058
dc.relation.referencesen27. Abdelwahab, A., Sekiya, H., Matsuba, I., Horiuchi, Y., & Kuroiwa, S. (2012). Alleviating the sparsity problem of collaborative filtering using an efficient iterative clustered prediction technique. International Journal of Information Technology & Decision Making, 11(01), 33–53. https://doi.org/10.1142/S0219622012500022
dc.relation.referencesen28. Martins, G. B., Papa, J. P., & Adeli, H. (2020). Deep learning techniques for recommender systems based on collaborative filtering. Expert Systems, 37(6), e12647. https://doi.org/10.1111/exsy.12647
dc.relation.referencesen29. Alabdulrahman, R., & Viktor, H. (2021). Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning. Expert Systems with Applications, 166, 114061. https://doi.org/10.1016/j.eswa.2020.114061.
dc.relation.referencesen30. Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. http://doi.org/10.1509/jmkr.37.3.363.18779
dc.relation.referencesen31. Basilico, J., & Hofmann, T. (2004, July). Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning (p. 9). https://doi.org/10.1145/1015330.1015394
dc.relation.referencesen32. Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487–1524. DOI: 10.3233/IDA-163209.
dc.relation.referencesen33. CPU-Z System information software. URL: https://www.cpuid.com/softwares/cpu-z.html
dc.relation.referencesen34. GPU-Z Main settings and features. URL: https://www.techpowerup.com/gpuz/
dc.relation.referencesen35. AIDA64 Extreme settings. URL: https://www.aida64.com/products/aida64-extreme
dc.relation.referencesen36. Speccy main projects and opportunities. URL: https://www.ccleaner.com/speccy
dc.relation.referencesen37. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge based systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
dc.relation.referencesen38. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734–749. DOI: 10.1109/TKDE.2005.99
dc.relation.referencesen39. Lobur, M. V., Schwartz, M. E., & Stech, Y. V. (2018). Models and methods of forecasting recommendations for collaborative recommender systems. Bulletin of the Lviv Polytechnic National University. Series: Information systems and networks, (901), 68–75. URL: https://science.lpnu.ua/sites/default/files/journal-paper/2019/feb/15581/181912maket-68-75.pdf
dc.relation.referencesen40. Masthoff, J. (2004). Group modeling: Selecting a sequence of television items to suit a group of viewers. Personalized Digital Television: Targeting Programs to individual Viewers, 93–141. https://doi.org/10.1007/1-4020-2164-X_5
dc.relation.referencesen41. Guha, S., Rastogi, R., & Shim, K. (2000). ROCK: A robust clustering algorithm for categorical attributes. Information systems, 25(5), 345–366. https://doi.org/10.1016/S0306-4379(00)00022-3.
dc.relation.referencesen42. Burke, R. (2007). Hybrid web recommender systems. The adaptive web, 377–408. URL: https://link.springer.com/chapter/10.1007/978-3-540-72079-9_12
dc.relation.referencesen43. 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.referencesen44. 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. URL: https://www.learntechlib.org/p/219036/
dc.relation.referencesen45. 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.referencesen46. 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.referencesen47. 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.referencesen48. Johnson, J. (2007). GUI bloopers 2.0: common user interface design don'ts and dos. Elsevier.
dc.relation.referencesen49. Parush, A. (2015). Conceptual design for interactive systems: designing for performance and user experience. Morgan Kaufmann.
dc.relation.referencesen50. 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.referencesen51. 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.referencesen52. OMG® Unified Modeling Language® (OMG UML®). URL: https://www.omg.org/spec/UML/2.5.1/PDF
dc.relation.urihttps://www.techradar.com/best/best-benchmarks-software
dc.relation.urihttps://doi.org/10.23939/sisn2022.11.127
dc.relation.urihttps://ceur-ws.org/Vol-3312/paper22.pdf
dc.relation.urihttps://doi.org/10.3390/a13080176
dc.relation.urihttps://doi.org/10.1007/s10844-021-00674-5
dc.relation.urihttps://doi.org/10.1016/j.eswa.2020.114324
dc.relation.urihttps://doi.org/10.1016/j.future.2017.04.028
dc.relation.urihttp://doi.org/10.1145/138859.138860
dc.relation.urihttp://doi.org/10.1109/MIS.2006.4
dc.relation.urihttps://doi.org/10.1016/j.compeleceng.2022.108571
dc.relation.urihttps://doi.org/10.1016/j.eswa.2022.118317
dc.relation.urihttps://doi.org/10.1145/3523227.3546782
dc.relation.urihttp://doi.org/10.1145/138859.138867
dc.relation.urihttps://doi.org/10.1145/192844.192905
dc.relation.urihttps://doi.org/10.1145/2346536.2346592
dc.relation.urihttp://doi.org/10.1016/j.eswa.2017.09.058
dc.relation.urihttps://doi.org/10.1142/S0219622012500022
dc.relation.urihttps://doi.org/10.1111/exsy.12647
dc.relation.urihttps://doi.org/10.1016/j.eswa.2020.114061
dc.relation.urihttp://doi.org/10.1509/jmkr.37.3.363.18779
dc.relation.urihttps://doi.org/10.1145/1015330.1015394
dc.relation.urihttps://www.cpuid.com/softwares/cpu-z.html
dc.relation.urihttps://www.techpowerup.com/gpuz/
dc.relation.urihttps://www.aida64.com/products/aida64-extreme
dc.relation.urihttps://www.ccleaner.com/speccy
dc.relation.urihttps://doi.org/10.1016/j.knosys.2013.03.012
dc.relation.urihttps://science.lpnu.ua/sites/default/files/journal-paper/2019/feb/15581/181912maket-68-75.pdf
dc.relation.urihttps://doi.org/10.1007/1-4020-2164-X_5
dc.relation.urihttps://doi.org/10.1016/S0306-4379(00)00022-3
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-540-72079-9_12
dc.relation.urihttps://www.learntechlib.org/p/219036/
dc.relation.urihttps://www.omg.org/spec/UML/2.5.1/PDF
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.rights.holder© Верес О. М., Гадзало О. Я., 2023
dc.subjectаналіз
dc.subjectкомп’ютерні комплектуючі
dc.subjectметоди кластеризації
dc.subjectметоди рекомендацій
dc.subjectрекомендаційна система
dc.subjectanalysis
dc.subjectcomputer components
dc.subjectclustering methods
dc.subjectmethods of recommendations
dc.subjectrecommendation system
dc.subject.udc004.8
dc.titleЗастосування методів рекомендацій в процесах аналізу комп’ютерних комплектуючих
dc.title.alternativeApplication of methods of recommendations in the analysis of computer components
dc.typeArticle

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
2023n14_Veres_O-Application_of_methods_of_recommendations_84-98.pdf
Size:
9.89 MB
Format:
Adobe Portable Document Format
Download
Thumbnail Image
Name:
2023n14_Veres_O-Application_of_methods_of_recommendations_84-98__COVER.png
Size:
417.33 KB
Format:
Portable Network Graphics
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.8 KB
Format:
Plain Text
Description:
Download

Collections

Вісник Національного університету "Львівська політехніка". Інформаційні системи та мережі. – 2023. – Випуск 14