A Comprehensive Review on Machine Learning-Based Job Recommendation Systems
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Abstract
A dynamic, constantly shifting labor market creates enormous job postings, overwhelming candidates and making it difficult for businesses to find quality candidates. It is also hard for job seekers to find suitable jobs. Addressing these issues, machine learning-driven job recommender systems have recently become an essential tool using predictive models to improve the match between jobs and candidates. A hybrid design that combines collaborative filtering with content-based filtering and adds contextual information like geographic location, industry trends, and user behavioural data can enhance the accuracy and relevance of recommendations. This paper reviews and critically analyzes contemporary job recommender system techniques. The focus is on hybrid recommendation models and the integration of algorithmic approaches, indicating their strengths and weaknesses. This review also looks into the evaluation metrics like precision, recall, normalized discounted cumulative gain (NDCG), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). To provide an overall perspective of the various approaches employed and the performance trade-offs inherent therein, this paper hopes to shed some light on the optimization of job recommendation systems for better effectiveness and user satisfaction.
Manuscript received:19 Mar 2025 | Revised: 29 Apr 2025 | Accepted: 10 May 2025 | Published: 30 Jul 2025
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References
X. Gu, L. Jian, C. Rao, Z. Bu and X. Cheng, “Together is better: knowledge-aware model with resume fusion for online job recommendation,” ACM Transactions on Knowledge Discovery from Data, vol. 19, no. 3, pp. 1-15, 2025.
DOI: https://doi.org/10.1145/3716503
T. Anwar, V. Uma, M.I. Hussain and M. Pantula, “Collaborative filtering and kNN based recommendation to overcome cold start and sparsity issues: A comparative analysis,” Multimedia Tools and Applications, vol. 81, no. 25, pp. 35693-35711, 2022.
DOI: https://doi.org/10.1007/s11042-021-11883-z
A.V. Chandak, H. Pandey, G. Rushiya and H. Sharma, “Resume Parser and Job Recommendation System using Machine Learning,” 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 157–162, 2024.
DOI: https://doi.org/10.1109/ESIC60604.2024.10481635
D. Shin, “How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance,” Computers in Human Behavior, vol. 109, pp. 106344, 2020.
DOI: https://doi.org/10.1016/j.chb.2020.106344
Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim and R. Kashef, “Recommendation systems: Algorithms, challenges, metrics, and business opportunities,” Applied Sciences (Switzerland), vol. 10, no. 21, 2020.
DOI: https://doi.org/10.3390/app10217748
G.M. Dhananjaya, R.H. Goudar, A.A. Kulkarni, V.N. Rathod and G.S. Hukkeri, “A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review,” IEEE Access, vol. 12, 2024.
DOI: https://doi.org/10.1109/ACCESS.2024.3369901
Y.M. Latha and B.S. Rao, “Product recommendation using enhanced convolutional neural network for e-commerce platform,” Cluster Computing, vol. 27, no. 2, pp. 1639-1653, 2024.
DOI: https://doi.org/10.1007/s10586-023-04053-3
A. Gugnani and H. Misra, “Implicit skills extraction using document embedding and its use in job recommendation,” AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020.
DOI: https://doi.org/10.1609/aaai.v34i08.7038
D. Mhamdi, R. Moulouki, M.Y. El Ghoumari, M. Azzouazi and L. Moussaid, “Job recommendation based on job profile clustering and job seeker behavior,” Procedia Computer Science, 2020.
DOI: https://doi.org/10.1016/j.procs.2020.07.102
Y. Mao, Y. Cheng and C. Shi, “A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition,” Applied Sciences, vol. 13, no. 16, p. 9464, 2023.
DOI: https://doi.org/10.3390/app13169464
E.S. Negara, Sulaiman, R. Andryani, P.H. Saksono, and Y. Widyanti, “Recommendation System with Content-Based Filtering in NFT Marketplace,” Journal of Advances in Information Technology, vol. 14, no. 3, 2023.
DOI: https://doi.org/10.12720/jait.14.3.518-522
S.M. Sakti, A.D. Laksito, B.W. Sari and D. Prabowo, “Music Recommendation System Using Content-based Filtering Method with Euclidean Distance Algorithm,” Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, 2022.
DOI: https://doi.org/10.1109/ICITISEE57756.2022.10057753
R. Lara-Cabrera, Á. González-Prieto, F. Ortega and J. Bobadilla, "Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming," Applied Sciences, vol. 10, no. 2, pp. 675, 2020.
DOI: https://doi.org/10.3390/app10020675
L. Chen, Y. Yuan, J. Yang and A. Zahir, “Improving the prediction quality in memory-based collaborative filtering using categorical features,” Electronics, vol. 10, no. 2, 2021.
DOI: https://doi.org/10.3390/electronics10020214
F. Horasan, A.H. Yurttakal and S. Gündüz, “A novel model based collaborative filtering recommender system via truncated ULV decomposition,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 8, 2023
DOI: https://doi.org/10.1016/j.jksuci.2023.101724
I. Mazlan, N. Abdullah and N. Ahmad, “Exploring the Impact of Hybrid Recommender Systems on Personalized Mental Health Recommendations,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 6, 2023.
DOI: https://doi.org/10.14569/IJACSA.2023.0140699
F. García-Sánchez, R. Colomo-Palacios and R. Valencia-García, “A social-semantic recommender system for advertisements,” Information Processing & Management, vol. 57, no. 2, pp. 102153, 2020.
DOI: https://doi.org/10.1016/j.ipm.2019.102153
M. Kartheek and G.P. Sajeev, “Building Semantic Based Recommender System Using Knowledge Graph Embedding,” Proceedings of the IEEE International Conference Image Information Processing, 2021.
DOI: https://doi.org/10.1109/ICIIP53038.2021.9702632
A. Salama, M.E. Shaheen and H. Alfeel, “Rule-based Recommendation System based on Semantic Web of Things,” International Journal of Engineering Research and Technology, vol. 13, no. 6, 2020.
DOI: https://doi.org/10.37624/ijert/13.6.2020.1455-1465
B. Loepp and J. Ziegler, “Exploring the Potential of Generative AI for Augmenting Choice-Based Preference Elicitation in Recommender Systems,” Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 114–119, 2024.
DOI: https://doi.org/10.1145/3631700.3664873
Y. Deldjoo, Z. He, J. McAuley, A. Korikov, S. Sanner, A. Ramisa, R. Vidal, M. Sathiamoorthy, A. Kasirzadeh and S. Milano, "A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)," Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 6448-6458, 2024.
DOI: https://doi.org/10.1145/3637528.3671474
A.D. Mahajan, A. Mahale, A.S. Deshmukh, A. Vidyadharan, V.S. Hegde and K. Vijayaraghavan, “Generative AI-Powered Spark Cluster Recommendation Engine,” Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, 2023.
DOI: https://doi.org/10.1109/ICAISS58487.2023.10250700
M. Loukili, F. Messaoudi, and M. El Ghazi, “Machine learning based recommender system for e-commerce,” IAES International Journal of Artificial Intelligence, vol. 12, no. 4, 2023.
DOI: https://doi.org/10.11591/ijai.v12.i4.pp1803-1811
L. Zhu and G. Goldsztein, “Predicting the Chance of Heart Attack with a Machine Learning Approach – Supervised Learning,” Journal of Student Research, vol. 11, no. 3, 2022.
DOI: https://doi.org/10.47611/jsrhs.v11i3.3380
M. Tabany and M. Gueffal, “Sentiment Analysis and Fake Amazon Reviews Classification Using SVM Supervised Machine Learning Model,” Journal of Advances in Information Technology, vol. 15, no. 1, 2024.
DOI: https://doi.org/10.12720/jait.15.1.49-58
O. Iparraguirre-Villanueva, V. Guevara-Ponce, F. Sierra-Liñan, S. Beltozar-Clemente and M. Cabanillas-Carbonell, “Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 6, 2022.
DOI: https://doi.org/10.14569/IJACSA.2022.0130669
J. Trogh, W. Joseph, L. Martens and D. Plets, “An unsupervised learning technique to optimize radio maps for indoor localization,” Sensors, vol. 19, no. 4, 2019.
DOI: https://doi.org/10.3390/s19040752
X. Yang, Z. Song, I. King and Z. Xu, "A Survey on Deep Semi-Supervised Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 8934-8954, 2023.
DOI: https://doi.org/10.1109/TKDE.2022.3220219
J. Eckardt, M. Bornhäuser, K. Wendt and J.M. Middeke, "Semi-supervised learning in cancer diagnostics," Frontiers in Oncology, vol. 12, 2022.
DOI: https://doi.org/10.3389/fonc.2022.960984
J.M. Ramírez-Sanz, J. Maestro-Prieto, Á. Arnaiz-González and A. Bustillo, "Semi-supervised learning for industrial fault detection and diagnosis: A systemic review," ISA Transactions, vol. 143, pp. 255-270, 2023.
DOI: https://doi.org/10.1016/j.isatra.2023.09.027
X. Chen, L. Yao, J. McAuley, G. Zhou and X. Wang, "Deep reinforcement learning in recommender systems: A survey and new perspectives," Knowledge-Based Systems, vol. 264, pp. 110335, 2023.
DOI: https://doi.org/10.1016/j.knosys.2023.110335
R. Liu, F. Nageotte, P. Zanne, M. de Mathelin, and B. Dresp-Langley, “Deep reinforcement learning for the control of robotic manipulation: A focussed mini-review,” Robotics, vol. 10, no. 1, 2021.
DOI: https://doi.org/10.3390/robotics10010022
R.K. Halder, M.N. Uddin, M.A. Uddin, S. Aryal and A. Khraisat, "Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications," Journal of Big Data, vol. 11, no. 1, 2024.
DOI: https://doi.org/10.1186/s40537-024-00973-y
B. S, P. Majumdar and D. Hemanth, "Career Compass: Predictive Analytics and Personalized Job Recommendation System," SSRN Electronic Journal, 2025.
DOI: https://doi.org/10.2139/ssrn.5086025
D. Jannach, A. Manzoor, W. Cai and L. Chen, "A Survey on Conversational Recommender Systems," ACM Computing Surveys, vol. 54, no. 5, pp. 1-36, 2022.
DOI: https://doi.org/10.1145/3453154
P. Castells and A. Moffat, "Offline recommender system evaluation: Challenges and new directions," AI Magazine, vol. 43, no. 2, pp. 225-238, 2022.
DOI: https://doi.org/10.1002/aaai.12051
D. Jin, L. Wang, H. Zhang, Y. Zheng, W. Ding, F. Xia and S. Pan, "A survey on fairness-aware recommender systems," Information Fusion, vol. 100, pp. 101906, 2023.
DOI: https://doi.org/10.1016/j.inffus.2023.101906
I.D. Raji and J. Buolamwini, "Actionable Auditing Revisited," Communications of the ACM, vol. 66, no. 1, pp. 101-108, 2023.
DOI: https://doi.org/10.1145/3571151
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman and A. Galstyan, "A Survey on Bias and Fairness in Machine Learning," ACM Computing Surveys, vol. 54, no. 6, pp. 1-35, 2022.
DOI: https://doi.org/10.1145/3457607
Y. Zhao, Y. Wang, Y. Liu, X. Cheng, C.C. Aggarwal and T. Derr, "Fairness and Diversity in Recommender Systems: A Survey," ACM Transactions on Intelligent Systems and Technology, vol. 16, no. 1, pp. 1-28, 2025.
DOI: https://doi.org/10.1145/3664928
K. Appadoo, M. B. Soonnoo, and Z. Mungloo-Dilmohamud, “JobFit: Job Recommendation using Machine Learning and Recommendation Engine,” 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020.
DOI: https://doi.org/10.1109/CSDE50874.2020.9411584
C.D. Ruijt and S. Bhulai, "Job Recommender Systems: A Review," arXiv, 2021.
DOI: https://doi.org/10.48550/arXiv.2111.13576
M.N. Freire and L.N.d. Castro, "e-Recruitment recommender systems: a systematic review," Knowledge and Information Systems, vol. 63, no. 1, pp. 1-20, 2021.
DOI: https://doi.org/10.1007/s10115-020-01522-8
B. Parida, P. KumarPatra, and S. Mohanty, “Prediction of recommendations for employment utilizing machine learning procedures and geo-area based recommender framework,” Sustainable Operations and Computers, vol. 3, pp. 83–92, 2022.
DOI: https://doi.org/10.1016/j.susoc.2021.11.001
Y.F. Simanjuntak and A. Wibowo, “RECOMMENDATION SYSTEM FOR ONLINE JOB VACANCY USING MACHINE LEARNING MODELS,” Journal of Theoretical Applied Information Technology, vol. 101, no. 2, 2023.
URL: https://www.jatit.org/volumes/Vol101No2/28Vol101No2.pdf
D. Singh, N. Patel and U. Singh, “Method for Job Recommendation based on Machine Learning and Deep Learning Model,” 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS), pp. 875–883, 2023.
DOI: https://doi.org/10.1109/ICACRS58579.2023.10405321
Y. Mao, Y. Cheng and C. Shi, “A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition,” Applied Sciences, vol. 13, no. 16, p. 9464, 2023.
DOI: https://doi.org/10.3390/app13169464
S. Sankarasetty, S. Kakaraparti, M. Saiful Haq, and S. K. Jonnalagadda, “A Comparative Study on Job Recommendation using Classification Algorithms,” Proceedings - 7th International Conference on Computing Methodologies and Communication, 2023.
DOI: https://doi.org/10.1109/ICCMC56507.2023.10083728
A. Huamán, G. Rebaza and D. Subauste, "Hybrid Job Recommendation Model Based on Professional Profile Using Data from Job Boards and Machine Learning Libraries," Proceedings of the International Multi-Conference on Society, Cybernetics and Informatics, pp. 72-79, 2024.
DOI: https://doi.org/10.54808/IMSCI2024.01.72
G. K, M.R. K and A. Haldar, "Job Recommendation System using LinkedIn User Profiles," 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE), pp. 1-6, 2024.
DOI: https://doi.org/10.1109/ic-ETITE58242.2024.10493325
J.W.T. Lim, C. Tee and M.K.O. Goh, "Exploring Activities of Daily Living Among the Elderly through Machine Learning Techniques," International Journal on Robotics, Automation and Sciences, vol. 7, no. 1, pp. 35-46, 2025.
DOI: https://doi.org/10.33093/ijoras.2025.7.1.5
J.P. Cheng and S.C. Haw, "Mental Health Problems Prediction Using Machine Learning Techniques," International Journal on Robotics, Automation and Sciences, vol. 5, no. 2, pp. 59-72, 2023.