Recommender System: Design and Opportunity in the Development of Information Systems

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Published Dec 31, 2023
https://doi.org/10.69899/edtech-en-230302058u
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Vol. 3 No. 2 (2023): Scientific Journal for Contemporary Education and Application of Information Technologies – EdTech Journal

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Andrej Unković
Information Techonolgy School – ITS, Belgrade, Serbia
Aleksandar Simović
Information Techonolgy School – ITS, Belgrade, Serbia

Abstract

This paper investigates the design and opportunity of recommendation engines in developing information systems. The essential characteristics of the recommendation system, technological tools for monitoring and collecting user activities, and the method of generating recommendations are defined. The main focus is on the basic concepts, different types of recommendation systems, and their practical application in different technological fields and industries. Also, the paper analyzes the most used algorithms for generating recommendations and various metrics for evaluation. It shows the most common problems faced by system recommendations, the most important of which are the problems of cold start, sparse data, and changes in user behavior. Applied recommendation systems in current areas such as e-commerce, video streaming platforms, and social networks are discussed. At the end of the paper, the future development of the recommendation system, the future direction of research within information systems, and the potential improvement of user experience within various information systems were discussed. The paper aims to provide a clearer insight into recommendation systems and their increasingly pronounced impact on users' online activities.

Keywords

recommender systems, applied recommender algorithms, personalized systems, hybrid recommendation, performance evaluation

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported License.

How to Cite
Unković, A. ., & Simović, A. . (2023). Recommender System: Design and Opportunity in the Development of Information Systems. EdTech Journal, 3(2), 53–62. https://doi.org/10.69899/edtech-en-230302058u
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References

  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