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

Abstract

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.

Keywords


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

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