Abstract

With the rapid development of internet applications and the continuous growth of data, there is an increased need for recommendation systems that can filter, prioritize, and efficiently deliver relevant information. In order to mitigate the problem of information overload, these systems provides suggestions or recommendations for personalized information. Recommendation systems address the challenges by processing large volumes of data and delivering personalized contents and services. Recommendation systems can be classified into similarity-based and AI-based approaches, each having its own strengths, and limitations which make them suitable for different application scenarios. This paper presents a comprehensive study of similarity-based and AI-based recommendation systems, focusing on their methodologies, applications, usability and suggests suitable recommendations models for real-time applicability. Furthermore, this analysis highlights key strengths and limitations of each approaches such as performance, scalability, and system limitations, offering insights into their effectiveness and applicability to modern recommendation tasks.

Author

Atul Sahu1,*, Chandrakant Kumar Singh2, A. K. Malik3  ( Pages 33-52 )
Email:atulsahu035@gmail.com
Affiliation:MCA Student, UP Rajarshi Tandon Open University Prayagraj, UP, India      DOI: https://doi.org/10.58517/IJSE.2025.04103

Keyword

Similarity-based recommendation system, AI-based recommendation system, Hybrid recommendation system, comparative study.

References

1.     Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, Article 114324. https://doi.org/10.1016/j.eswa.2020.114324

2.     Afoudi, Y., Lazaar, M., & Al Achhab, M. (2021). Hybrid recommendation system combining content-based filtering and collaborative prediction using artificial neural networks. Simulation Modelling Practice and Theory, 113, Article 102375. https://doi.org/10.1016/j.simpat.2021.102375

3.     Al Fararni, K., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., & Sabri, A. (2021). Hybrid recommender system for tourism based on big data and AI: A conceptual framework. Big Data Mining and Analytics, 4(1), 47–55.

4.     Alatrash, R., Ezaldeen, H., Misra, R., & Priyadarshini, R. (2021). Sentiment analysis using deep learning for recommendation in e-learning domain. In Progress in advanced computing and intelligent engineering (pp. 123–133). Springer. https://doi.org/10.1007/978-981-33-4299-6_10

5.     Al-Hassan, M., Abu-Salih, B., Alshdaifat, E., Aloqaily, A., & Rodan, A. (2024). An improved fusion-based semantic similarity measure for effective collaborative filtering recommendations. International Journal of Computational Intelligence Systems, 17, Article 45. https://doi.org/10.1007/s44196-024-00429-4

6.     Ali, Z., Huang, Y., Ullah, I., Feng, J., Deng, C., Thierry, N., Khan, A., Jan, A. U., Shen, X., Rui, W., & Qi, G. (2023). Deep learning for medication recommendation: A systematic survey. Data Intelligence, 5(2), 303–354. https://doi.org/10.1162/dint_a_00197

7.     Anand, R., Sabeenian, R. S., Gurang, D., Kirthika, R., & Rubeena, S. (2021). AI based music recommendation system using deep learning algorithms. IOP Conference Series: Earth and Environmental Science.

8.     Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, Article 103743.

9.     Chaudhari, A., Seddig, A. A. H., Sarlan, A., & Raut, R. (2024). A hybrid recommendation system: A review. IEEE Access, 12, 157107–157126.

10.  Chibb, M., Vashisht, P., Katti, A., & Nrang, A. (2024). Enhancing movie recommendations: A content-based approach using TF-IDF weighted Word2Vec and cosine similarity. In Proceedings of the 4th International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 1449–1454).

11.  Da Silva, F. L., Slodkowski, B. K., Da Silva, K. K. A., & Cazella, S. C. (2023). A systematic literature review on educational recommender systems for teaching and learning: Research trends, limitations, and opportunities. Education and Information Technologies, 28(3), 3289–3328.

12.  De Croon, R., Van Houdt, L., Htun, N. N., Štiglic, G., Vanden Abeele, V., & Verbert, K. (2021). Health recommender systems: Systematic review. Journal of Medical Internet Research, 23(6), Article e18035. https://doi.org/10.2196/18035

13.  Feng, J., Xia, Z., Feng, X., & Peng, J. (2021). RBPR: A hybrid model for the new user cold start problem in recommender systems. Knowledge-Based Systems, 214, Article 106732.

14.  Habil, S., El-Deeb, S., & El-Bassiouny, N. (2023). AI-based recommendation systems: The ultimate solution for market prediction and targeting. In The Palgrave handbook of interactive marketing (pp. 683–704). Palgrave Macmillan.

15.  Hasan, M. K., Habib, A. A., Shukur, Z., Ibrahim, F., Islam, S., & Razzaque, M. A. (2023). Review on cyber-physical and cyber-security systems in smart grids: Standards, protocols, constraints, and recommendations. Journal of Network and Computer Applications, 209, Article 103540.

16.  Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, Article 104684.

17.  Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261–273.

18.  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, 16(3), 274–306.

19.  Kethineni, K., Mekala, S. H., Kodali, M., Kota, V. V., & Jampani, J. P. (2024). A web-based agriculture recommendation system using deep learning for crops, fertilizers, and pesticides. In Proceedings of the International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST).

20.  Khanduri, S., & Prabakeran, S. (2022). Hybrid recommendation system with graph-based and collaborative filtering recommendation systems. In Proceedings of MysuruCon 2022.

21.  Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A survey of recommendation systems: Recommendation models, techniques, and application fields. Electronics, 11(1), Article 136.

22.  Lacroux, A., & Martin-Lacroux, C. (2022). Should I trust the artificial intelligence to recruit? Recruiters’ perceptions and behavior when faced with algorithm-based recommendation systems during resume screening. Frontiers in Psychology, 13, Article 893102.

23.  Lahoud, C., Moussa, S., Obeid, C., El Khoury, H., & Champin, P. A. (2023). A comparative analysis of different recommender systems for university major and career domain guidance. Education and Information Technologies, 28(7), 8733–8759.

24.  Liu, W., Guo, W., Liu, Y., Tang, R., & Wang, H. (2023). User behavior modeling with deep learning for recommendation: Recent advances. In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys) (pp. 1286–1287).

25.  Marcuzzo, M., Zangari, A., Albarelli, A., & Gasparetto, A. (2022). Recommendation systems: An insight into current development and future research challenges. IEEE Access, 10, 86578–86623.

26.  Masciari, E., Umair, A., & Ullah, M. H. (2024). A systematic literature review on AI-based recommendation systems and their ethical considerations. IEEE Access, 12, 121223–121241.

27.  Murad, D. F., Toha, M., Mayatopani, H., Wijanarko, B. D., Heryadi, Y., Dewi, M. A., & Leandros, R. (2023). Personalized recommendation system for online learning: An opportunity. In 2023 8th International Conference on Business and Industrial Research (ICBIR) (pp. 128–132). IEEE. https://doi.org/10.1109/ICBIR57571.2023.10147613

28.  Necula, S. C., & Păvăloaia, V. D. (2023). AI-driven recommendations: A systematic review of the state of the art in e-commerce. Applied Sciences, 13(9), Article 5531.

29.  Nyamathulla, S., & Dhanamjayulu, C. (2024). A review of battery energy storage systems and advanced battery management systems for different applications: Challenges and recommendations. Journal of Energy Storage, 86, Article 111179.

30.  Papastratis, I., Konstantinidis, D., Daras, P., & Dimitropoulos, K. (2024). AI nutrition recommendation using a deep generative model and ChatGPT. Scientific Reports, 14(1), Article 12345.

31.  Patel, K., & Patel, H. B. (2023). Multi-criteria agriculture recommendation system using machine learning for crop and fertilizer prediction. Current Agriculture Research Journal, 11(1), 137–149.

32.  Priyadharshini, A., Chakraborty, S., Kumar, A., & Pooniwala, O. R. (2021). Intelligent crop recommendation system using machine learning. In Proceedings of the 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 843–848).

33.  Raghavendra, C. K., & Srikantaiah, K. C. (2021). Similarity based collaborative filtering model for movie recommendation systems. In Proceedings of the 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1143–1147).

34.  Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), Article 59.

35.  Sellamuthu, S., Vaddadi, S. A., Venkata, S., Petwal, H., Hosur, R., & Mandala, V. (2023). AI-based recommendation model for effective decision-making to maximise ROI. Soft Computing. https://doi.org/10.1007/s00500-023-08731-7

36.  Seth, R., & Sharaff, A. (2022). A comparative overview of hybrid recommender systems: Review, challenges, and prospects. In Data mining and machine learning applications (pp. 57–98).

37.  Sharma, S., Rana, V., & Malhotra, M. (2022). Automatic recommendation system based on hybrid filtering algorithm. Education and Information Technologies, 27(2), 1523–1538.

38.  Sun, C., Li, X., Wen, J., Wang, X., Han, Z., & Leung, V. C. M. (2023). Federated deep reinforcement learning for recommendation-enabled edge caching in mobile edge-cloud computing networks. IEEE Journal on Selected Areas in Communications, 41(3), 690–705.

39.  Tavakoli, M., Faraji, A., Vrolijk, J., Molavi, M., Mol, S. T., & Kismihók, G. (2022). An AI-based open recommender system for personalized labor market-driven education. Advanced Engineering Informatics, 52, Article 101508.

40.  Tran, D. T., & Huh, J. H. (2023). New machine learning model based on the time factor for e-commerce recommendation systems. Journal of Supercomputing, 79(6), 6756–6801.

41.  Troussas, C., Krouska, A., Tselenti, P., Kardaras, D. K., & Barbounaki, S. (2023). Enhancing personalized educational content recommendation through cosine similarity-based knowledge graphs and contextual signals. Information, 14(9), Article 510.

42.  Urdaneta-Ponte, M. C., Mendez-Zorrilla, A., & Oleagordia-Ruiz, I. (2021). Recommendation systems for education: Systematic review. Electronics, 10(14), Article 1611.

43.  Veeramanickam, M. R. M., Dabade, M. S., P, S. R. M., Borhade, R. R., Barekar, S. S., Navarro, C., Roman-Concha, U., & Rodriguez, C. (2023). Smart education system to improve the learning system with CBR based recommendation system using IoT. Heliyon, 9(7), Article e17863. https://doi.org/10.1016/j.heliyon.2023.e17863

44.  Walek, B., & Fajmon, P. (2023). A hybrid recommender system for an online store using a fuzzy expert system. Expert Systems with Applications, 212, Article 118565. https://doi.org/10.1016/j.eswa.2022.118565

45.  Wang, W., Zhang, Y., Li, H., Wu, P., & Feng, F. (2023). Causal recommendation: Progresses and future directions. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 3473–3476). ACM. https://doi.org/10.1145/3539618.3594245

46.  Wu, K., & Chi, K. (2024). Enhanced e-commerce customer engagement: A comprehensive three-tiered recommendation system. Journal of Knowledge Learning and Science Technology, 2, 2959–6386.

47.  Wu, L., Zheng, Z., Qiu, Z., Wang, H., Gu, H., Shen, T., Qin, C., Zhu, C., Zhu, H., Liu, Q., Xiong, H., & Chen, E. (2024). A survey on large language models for recommendation. World Wide Web, 27(5), Article 45. https://doi.org/10.1007/s11280-024-01291-2

48.  Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., & Hou, T. (2021). A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nature Communications, 12(1), Article 6775. https://doi.org/10.1038/s41467-021-27137-3

49.  Yoon, J. H., & Choi, C. (2023). Real-time context-aware recommendation system for tourism. Sensors, 23(7), Article 3512.

50.  Zamanzadeh Darban, Z., & Valipour, M. H. (2022). GHRS: Graph-based hybrid recommendation system with application to movie recommendation. Expert Systems with Applications, 200, Article 116850.

51.  Zanon, A. L., Souza, L., Pressato, D., & Manzato, M. G. (2022). A user study with aspect-based sentiment analysis for similarity of items in content-based recommendations. Expert Systems, 39(8), Article e12945.

52.  Zhang, Z., Patra, B. G., Yaseen, A., Zhu, J., Sabharwal, R., Roberts, K., Cao, T., & Wu, H. (2023). Scholarly recommendation systems: A literature survey. Knowledge and Information Systems, 65(11), 4433–4478. https://doi.org/10.1007/s10115-023-01901-x

53.  Zhou, H., Wang, H., Yu, Z., Bin, G., Xiao, M., & Wu, J. (2024). Federated distributed deep reinforcement learning for recommendation-enabled edge caching. IEEE Transactions on Services Computing, 17(6), 3640–3656.