Sistem za preporuku: Dizajn i mogućnosti u razvoju informacionih sistema

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Objavljeno Dec 31, 2023
https://doi.org/10.69899/edtech-en-230302058u
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Vol. 3 Br. 2 (2023): Scientific Journal for Contemporary Education and Application of Information Technologies – EdTech Journal

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Andrej Unković
Visoka škola strukovnih studija za informacione tehnologije – ITS, Beograd, Srbija
Aleksandar Simović
Visoka škola strukovnih studija za informacione tehnologije – ITS, Beograd, Srbija

Apstrakt

U ovom radu se istražuje dizajn i mogućnosti sistema za preporuku u razvoju informacionih sistema. Definišu se osnovne karakteristike sistema za preporuku, tehnološki alati za praćenje i prikupljanje korisničkih aktivnosti, kao i metod generisanja preporuka. Glavni fokus je na osnovnim konceptima, različitim vrstama sistema za preporuku i njihovoj praktičnoj primeni u različitim tehnološkim poljima i industrijama. Takođe, rad analizira najčešće korišćene algoritme za generisanje preporuka i različite metrike evaluacije. Prikazuje najčešće probleme sa kojima se susreću sistemi za preporuku, od kojih su najvažniji problemi hladnog starta, retkih podataka i promene korisničkog ponašanja. Govori o primenjenim sistemima za preporuku u trenutnim oblastima kao što su elektronska trgovina, platforme za strimovanje video sadržaja i društvene mreže. Na kraju rada razmatra se budući razvoj sistema za preporuku, budući pravci istraživanja unutar informacionih sistema i potencijalno unapređenje korisničkog iskustva unutar različitih informacionih sistema. Cilj ovog rada je da pruži jasniji uvid u sisteme za preporuku i njihov sve izraženiji uticaj na onlajn aktivnosti korisnika.

Ključne reči

Sistemi za preporuku, primenjeni algoritmi za preporuke, personalizovani sistemi, hibridni sistem za preporuke, evaluacija performansi

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Kako citirati
Unković, A. ., & Simović, A. . (2023). Sistem za preporuku: Dizajn i mogućnosti u razvoju informacionih sistema. EdTech Journal, 3(2), 53–62. https://doi.org/10.69899/edtech-en-230302058u
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Reference

  1. Wang Y, Ma W, Zhang M, Liu Y, Ma S. A survey on the fairness of recommender systems. ACM Transactions on Information Systems. 2023 Feb 7;41(3):1-43. Available from: hĴps://doi.org/10.1145/3547333
  2. Jannach D, Manzoor A, Cai W, Chen L. A survey on conversational recommender systems. ACM Computing Surveys (CSUR). 2021 May 23;54(5):1-36. Available from: hĴps://doi.org/10.1145/3453154
  3. Simović A. A Big Data smart library recommender system for an educational institution. Library Hi Tech. 2018 Apr 19;36(3):498-523. Available from: hĴps://doi.org/10.1108/LHT-06-2017-0131
  4. Alamdari PM, Navimipour NJ, Hosseinzadeh M, Safaei AA, Darwesh A. A systematic study on the recommender systems in the E-commerce. Ieee Access. 2020 Jun 16;8:115694-716. Available from: hĴps://doi.org/10.1109/ACCESS.2020.3002803
  5. Simović A. Sistemi preporuke u e-trgovini. Sinteza 2014-Impact of the Internet on Business Activities in Serbia and Worldwide. 2014:846-52. Available from: hĴps://doi.org/10.15308/sinteza-2014-846-852
  6. Zhang S, Yao L, Sun A, Tay Y. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR). 2019 Feb 25;52(1):1-38. Available from: hĴps://doi.org/10.1145/3285029
  7. Panda DK, Ray S. Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review. Journal of Intelligent Information Systems. 2022 Oct;59(2):341-66. Available from: hĴps://doi.org/10.1007/s10844-022-00698-5
  8. Rodpysh KV, Mirabedini SJ, Banirostam T. Resolving cold start and sparse data challenge in recommender systems using multi-level singular value decomposition. Computers & Electrical Engineering. 2021 Sep 1;94:107361. Available from: hĴps://doi.org/10.1016/j.compeleceng.2021.107361
  9. Kuznetsov S, Kordík P. Overcoming the cold-start problem in recommendation systems with ontologies and knowledge graphs. InEuropean Conference on Advances in Databases and Information Systems 2023 Aug 31 (pp. 591-603). Cham: Springer Nature Switzerland. Available from: hĴps://doi.org/10.1007/978-3-031-42941-5_52
  10. Start To Flow, Guide to Recommendation System: Types, Selection Criteria, How to Build One. Available from: hĴps://stratoflow.com/guide-to-recommendation-system
  11. Medium, Recommendation Systems Explained: Understanding the Basic to Advance. Available from: hĴps://utsavdesai26.medium.com/recommendation-systems-explained-understanding-the-basic-to-advance-43a5fce77c47
  12. Medium, Essentials of recommendation engines: content-based and collaborative filtering. Available from: hĴps://towardsdatascience.com/essentials-of-recommendation-engines-content-based-andcollaborative-filtering-31521c964922
  13. Isinkaye FO, Folajimi YO, Ojokoh BA. Recommendation systems: Principles, methods and evaluation. Egyptian informatics journal. 2015 Nov 1;16(3):261-73. Available from: hĴps://doi.org/10.1016/j.eij.2015.06.005
  14. Simović A. Recommender systems in the big data environment using Mahout framework. In2017 25th Telecommunication Forum (TELFOR) 2017 Nov 21 (pp. 1-4). IEEE. Available from: hĴps://doi.org/10.1109/TELFOR.2017.8249472
  15. Silveira T, Zhang M, Lin X, Liu Y, Ma S. How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics. 2019 May 1;10:813-31. Available from: hĴps://doi.org/10.1007/s13042-017-0762-9
  16. Khanal SS, Prasad PW, Alsadoon A, Maag A. A systematic review: machine learning based recommendation systems for e-learning. Education and Information Technologies. 2020 Jul;25:2635-64. Available from: hĴps://doi.org/10.1007/s10639-019-10063-9
  17. Mekić, N. Cloud Gaming Services. Scientific Journal for Contemporary Education and Application of Information Technologies – EdTech Journal, 2023. Available from: hĴps://doi.org/10.18485/edtech.2023.3.1.4
  18. Sun C, Li H, Li X, Wen J, Xiong Q, Zhou W. Convergence of recommender systems and edge computing: A comprehensive survey. IEEE Access. 2020 Mar 6;8:47118-32. Available from: hĴps://doi.org/10.1109/ACCESS.2020.2978896
  19. Schmidt S. Assessing the quality of experience of cloud gaming services. Springer Nature; 2022 Sep 7.
  20. Mekić N, Simović A. Web and Android Recommender System for Streaming Services using Apache Drill. Challenges of Contemporary Higher Education (CCHE). 2024. Available from hĴps://cche.rs/
  21. Deldjoo Y, Schedl M, Cremonesi P, Pasi G. Recommender systems leveraging multimedia content. ACM Computing Surveys (CSUR). 2020 Sep 28;53(5):1-38. Available from: hĴps://doi.org/10.1145/3407190
  22. Eirinaki M, Gao J, Varlamis I, Tserpes K. Recommender systems for large-scale social networks: A review of challenges and solutions. Future generation computer systems. 2018 Jan 1;78:413-8. Available from: hĴps://doi.org/10.1016/j.future.2017.09.015
  23. Daud NN, Ab Hamid SH, Saadoon M, Sahran F, Anuar NB. Applications of link prediction in social networks: A review. Journal of Network and Computer Applications. 2020 Sep 15;166:102716. Available from: hĴps://doi.org/10.1016/j.jnca.2020.102716
  24. Urdaneta-Ponte MC, Mendez-Zorrilla A, Oleagordia-Ruiz I. Recommendation systems for education: Systematic review. Electronics. 2021 Jul 6;10(14):1611. Available from: hĴps://doi.org/10.3390/electronics10141611
  25. Salazar C, Aguilar J, Monsalve-Pulido J, Montoya E. Affective recommender systems in the educational field. A systematic literature review. Computer Science Review. 2021 May 1;40:100377. Available from: hĴps://doi.org/10.1016/j.cosrev.2021.100377
  26. Pramanik PK, Choudhury P. IoT data processing: The different archetypes and their security and privacy assessment. InInternet of Things Security 2022 Sep 1 (pp. 37-54). River Publishers.
  27. Gupta D. A comprehensive study of recommender systems for the internet of things. InJournal of Physics: Conference Series 2021 Jul 1 (Vol. 1969, No. 1, p. 012045). IOP Publishing. Available from: hĴps://doi.org/10.1088/1742-6596/1969/1/012045
  28. Bourke S. The application of recommender systems in a multi site, multi domain environment. InProceedings of the 9th ACM Conference on Recommender Systems 2015 Sep 16 (pp. 229-229). Available from: hĴps://doi.org/10.1145/2792838.2799495