Implementation Graph Sampling and Aggregation (GraphSAGE) Method for Job Recommendation System

 Dewa Made Wijaya (Telkom University, Bandung, Indonesia)
 (*)Kemas Rahmat Saleh Wiharja Mail (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

Submitted: December 8, 2023; Published: January 9, 2024


Finding job is currently a challenge, especially for final-year students. Career Development Centre (CDC) is a service that is provided by a university for its students. However, a more sophisticated system is needed that not only provides job information but provides job recommendations based on their interests, skills, and experience. Developing a GraphSAGE-based job recommendation system can help provide suitable jobs according to user preferences. GraphSAGE works by embedding nodes or feature vectors at each node or node in a graph. GraphSAGE aggregates information from neighbouring nodes and propagates that information using different model layers. By combining the feature information of each node, the resulting representation can be richer in information and also more accurate. The development of the GraphSAGE system uses a dataset from the "Job Recommendation Challenge" from Kaggle which consists of 3 data, namely job data, user dataset, and applicant dataset. This study also uses GAT to provide a value or weight for each node before GraphSAGE process the graph. Based on experimental results, this GraphSAGE model has an accuracy value of 97.5% and this value is 13% greater than its comparison, namely FNN (Feedforward Neural Network) commonly used at tabular dataset. This comparison helps us know that which the best model we have to use to the dataset. The model also tested on the Movie dataset, Food dataset, and Epinions dataset.


Recommendations; Jobs; GraphSAGE; Embedding; Graph Attention Network; Feedforward Neural Network

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P. Jobseeker et al., Persaingan jobseeker bagi freshgraduate di era milenial, Sahmiyya, vol. 1, no. 1, pp. 150156, 2022, [Online]. Available:

W. Shalaby et al., Help me find a job: A graph-based approach for job recommendation at scale, Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-Janua, pp. 15441553, 2017, doi: 10.1109/BigData.2017.8258088.

S. Rahmawati, D. Nurjanah, and R. Rismala, Analisis dan Implementasi pendekatan Hybrid untuk Sistem Rekomendasi Pekerjaan dengan Metode Knowledge Based dan Collaborative Filtering, Indones. J. Comput., vol. 3, no. 2, p. 11, 2018, doi: 10.21108/indojc.2018.3.2.210.

S. Wu, F. Sun, W. Zhang, X. Xie, and B. Cui, Graph Neural Networks in Recommender Systems: A Survey, ACM Comput. Surv., vol. 55, no. 5, 2022, doi: 10.1145/3535101.

Q. Guo et al., A Survey on Knowledge Graph-Based Recommender Systems, IEEE Trans. Knowl. Data Eng., vol. 34, no. 8, pp. 35493568, 2022, doi: 10.1109/TKDE.2020.3028705.

M. Naumov et al., Deep Learning Recommendation Model for Personalization and Recommendation Systems, 2019, [Online]. Available:

D. Aguado, J. C. Andrs, A. L. Garca-Izquierdo, and J. Rodrguez, LinkedIn Big Four: Job performance validation in the ICT sector, Rev. Psicol. del Trab. y las Organ., vol. 35, no. 2, pp. 5364, 2019, doi: 10.5093/jwop2019a7.

L. D. Kumalasari and A. Susanto, Recommendation System of Information Technology Jobs using Collaborative Filtering Method Based on LinkedIn Skills Endorsement, Sisforma, vol. 6, no. 2, pp. 6372, 2020, doi: 10.24167/sisforma.v6i2.2240.

M. H. Mohamed, M. H. Khafagy, and M. H. Ibrahim, Recommender Systems Challenges and Solutions Survey, Proc. 2019 Int. Conf. Innov. Trends Comput. Eng. ITCE 2019, no. February, pp. 149155, 2019, doi: 10.1109/ITCE.2019.8646645.

L. Chen, L. Wu, R. Hong, K. Zhang, and M. Wang, Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach, AAAI 2020 - 34th AAAI Conf. Artif. Intell., pp. 2734, 2020, doi: 10.1609/aaai.v34i01.5330.

H. Zhang, T. Zhou, T. Xu, Y. Wang, and H. Hu, FNN-Based Prediction of Wireless Channel with Atmospheric Duct, IEEE Int. Conf. Commun., no. April, 2021, doi: 10.1109/ICC42927.2021.9501068.

B. Markapudi, K. Chaduvula, D. N. V. S. L. S. Indira, and M. V. N. S. S. R. K. Sai Somayajulu, Content-based video recommendation system (CBVRS): a novel approach to predict videos using multilayer feed forward neural network and Monte Carlo sampling method, Multimed. Tools Appl., vol. 82, no. 5, pp. 69656991, 2023, doi: 10.1007/s11042-022-13583-8.

K. Xu, M. Zhang, J. Li, S. S. Du, K. I. Kawarabayashi, and S. Jegelka, How Neural Networks Extrapolate: From Feedforward To Graph Neural Networks, ICLR 2021 - 9th Int. Conf. Learn. Represent., 2021.

T. Bai, Y. Zhang, B. Wu, and J. Y. Nie, Temporal Graph Neural Networks for Social Recommendation, Proc. - 2020 IEEE Int. Conf. Big Data, Big Data 2020, pp. 898903, 2020, doi: 10.1109/BigData50022.2020.9378444.

M. Shi et al., Genetic-GNN: Evolutionary architecture search for Graph Neural Networks, Knowledge-Based Syst., vol. 247, 2022, doi: 10.1016/j.knosys.2022.108752.

D. El Alaoui, J. Riffi, A. Sabri, B. Aghoutane, A. Yahyaouy, and H. Tairi, Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network, Neural Comput. Appl., vol. 34, no. 14, pp. 1167911690, 2022, doi: 10.1007/s00521-022-07059-x.

L. Hickman, S. Thapa, L. Tay, M. Cao, and P. Srinivasan, Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations, Organ. Res. Methods, vol. 25, no. 1, pp. 114146, 2022, doi: 10.1177/1094428120971683.

A. Humeau-Heurtier, Texture feature extraction methods: A survey, IEEE Access, vol. 7, pp. 89759000, 2019, doi: 10.1109/ACCESS.2018.2890743.

K. Jani, M. Chaudhuri, H. Patel, and M. Shah, Machine learning in films: an approach towards automation in film censoring, J. Data, Inf. Manag., vol. 2, no. 1, pp. 5564, 2020, doi: 10.1007/s42488-019-00016-9.

H. Wang, M. Zhao, X. Xie, W. Li, and M. Guo, Knowledge graph convolutional networks for recommender systems, Web Conf. 2019 - Proc. World Wide Web Conf. WWW 2019, pp. 33073313, 2019, doi: 10.1145/3308558.3313417.

K. Wiharja, J. Z. Pan, M. Kollingbaum, and Y. Deng, More Is Better: Sequential Combinations of Knowledge Graph Embedding Approaches, in Semantic Technology, 2018, pp. 1935.

K. Wiharja, J. Z. Pan, M. Kollingbaum, and Y. Deng, Pattern-Based Reasoning to Investigate the Correctness of Knowledge Graphs, in 25th Automated Reasoning Workshop, 2018, p. 10.

A. Hogan et al., Knowledge graphs, ACM Comput. Surv., vol. 54, no. 4, 2021, doi: 10.1145/3447772.

A. Salehi and H. Davulcu, Graph Attention Auto-Encoders, CoRR, vol. abs/1905.10715, 2019, [Online]. Available:

P. Veli?kovi?, A. Casanova, P. Li, G. Cucurull, A. Romero, and Y. Bengio, Graph attention networks, 6th Int. Conf. Learn. Represent. ICLR 2018 - Conf. Track Proc., pp. 112, 2018, doi: 10.1007/978-3-031-01587-8_7.

V. P. Dwivedi and X. Bresson, A Generalization of Transformer Networks to Graphs, 2020, [Online]. Available:

N. R. Ananda, K. R. S. Wiharja, and M. A. Bijaksana, Sentiment Analysis on Banking Chatbot using Graph-based Machine Learning Model, in 2023 International Conference on Data Science and Its Applications (ICoDSA), 2023, pp. 310315. doi: 10.1109/ICoDSA58501.2023.10276448.

S. Racherla, Available Digital service, Graph Convolutional Networks, GraphSage, Recommendation system, PinSage Research Article Racherla, Res. Rev. Sci. Technol., vol. 3, no. 1, pp. 7993, 2020.

K. Wang, R. Mathews, C. Kiddon, H. Eichner, F. Beaufays, and D. Ramage, Federated Evaluation of On-device Personalization, 2019, [Online]. Available:

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