1. SISN
  2. All Volumes and Issues
  3. Volume 14, 2023
  4. Application of Methods of Recommendations in the Analysis of Computer Components

Application of Methods of Recommendations in the Analysis of Computer Components

SISN.
2023;
Volume 14
: pp. 84 - 98
https://doi.org/10.23939/sisn2023.14.084
Authors:
  1. Oleh Veres
  2. Oleh Hadzalo
1
Lviv Polytechnic National University, Information Systems and Network
2
Lviv Polytechnic National University, Ukraine

Today, 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.

analysis
computer components
clustering methods
methods of recommendations
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. DOI: 10.1007/978-1-4899-7687-1_964
  2. Pavlysh, V. A., Glinenko, L. K. & Shakhovska, N. B. (2018). Fundamentals of information technologies and systems: a textbook. Lviv: Department of Lviv Polytechnic.
  3. Best benchmarks and their pluses. URL: https://www.techradar.com/best/best-benchmarks-software
  4. 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.
  5. 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.
  6. 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
  7. 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
  8. 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
  9. 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
  10. Falk, K. (2019). Practical recommender systems. Simon and Schuster.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Loeb, S., & Terry, D. (1992). Information filtering. Communications of the ACM, 35(12), 26–28. http://doi.org/10.1145/138859.138860.
  16. 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.
  17. 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.
  18. 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
  19. 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
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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.
  30. Ansari, A., Essegaier, S., &  Kohli, R. (2000). Internet recommendation systems. http://doi.org/10.1509/jmkr.37.3.363.18779
  31. 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
  32. Çano, E., & Morisio, M. (2017). Hybrid recommender systems: A systematic literature review. Intelligent Data Analysis, 21(6), 1487–1524. DOI: 10.3233/IDA-163209.
  33. CPU-Z System information software. URL: https://www.cpuid.com/softwares/cpu-z.html
  34. GPU-Z Main settings and features. URL: https://www.techpowerup.com/gpuz/
  35. AIDA64 Extreme settings. URL: https://www.aida64.com/products/aida64-extreme
  36. Speccy main projects and opportunities. URL: https://www.ccleaner.com/speccy
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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.
  42. 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
  43. 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
  44. 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/
  45. 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
  46. 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
  47. 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
  48. Johnson, J. (2007). GUI bloopers 2.0: common user interface design don'ts and dos. Elsevier.
  49. Parush, A. (2015). Conceptual design for interactive systems: designing for performance and user experience. Morgan Kaufmann.
  50. 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
  51. 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
  52. OMG® Unified Modeling Language® (OMG UML®). URL: https://www.omg.org/spec/ UML/2.5.1/PDF