Unemployed individuals and recent graduates often lack the experience or skills necessary to effectively search and filter job positions on job search websites, hindering their ability to access job listings and requirements. To address this issue, a research process was conducted involving extracting job information from the top job search website in Thailand. The data was then prepared, transformed, and subjected to Matrix Factorization. The data was randomly split into 80% for training and 20% for testing, out of a total of 30,000 records. This data was used to test the performance of a recommendation system utilizing the Collaborative Filtering technique. The Learning Curve, which measures the efficiency of model learning through Training Loss and Validation Loss, showed a continuous decrease in value until it reached its lowest validation loss of 0.0442, indicating a well-fitted model. This research suggests that the model can be effectively used for predicting and creating a prototype of a job recommendation system that connects job opportunities via API to efficiently meet the needs of job seekers and recent graduates.

This research presents an integrated machine learning approach for optimizing digital workforce matching in Thailand's evolving digital economy. The study develops a novel job recommendation system combining Natural Language Processing (NLP) with Random Forest classification to analyze job market data from Thailand's leading recruitment platforms. Using FastText for initial job classification and a Random Forest model for skill-based matching, the system achieves 75% accuracy in job recommendations across 20 digital job categories. The methodology incorporates automated skill extraction, cross-validated model comparison, and a user-friendly web interface for practical applications. Our findings reveal distinct skill clusters and job-skill relationships in Thailand's digital sector, with the Random Forest model outperforming traditional Decision Tree approaches by 4% in accuracy metrics. The system demonstrates robust performance in real-world testing, achieving 86.67% accuracy in matching previously unseen job postings. This research contributes to both theoretical understanding of skill-based job matching and practical workforce development, offering insights for curriculum development and career planning for workforce development stakeholders in Thailand's digital sector.

The integration of sequential and social recommendations using a graph-based co-attention architecture marks a significant advancement in deep learning, substantially improving recommender system performance. This innovation enables effective representation learning of user interactions by capturing dynamic preferences influenced by both sequential and social contexts. While prior research has attempted to model evolving user preferences, existing approaches suffer from two key limitations: 1) ineffective modeling of complex sequential dependencies and 2) overlooking the hierarchical nature of social influence, resulting in suboptimal performance. To address these challenges, we propose GCA4Rec (Graph-Based Co-Attention Networks for Sequential and Social Recommendation), a novel framework designed to handle the complexities of user preference dynamics across sequential and social domains. Our model combines a graph attention contrastive learning module to track sequential preference shifts, while a hyperbolic graph attention isomorphism network models social-level preference dynamics. Additionally, we introduce fusionGated, a novel gating mechanism that effectively integrates co-attention signals from both levels. Extensive experiments on real-world benchmark datasets demonstrate that GCA4Rec outperforms state-of-the-art methods in Top-k recommendation tasks and exhibits robustness under varying degrees of data sparsity.

Sequential-social recommendation systems are essential for understanding users’ evolving interests and predicting their future behaviors. While existing methods employing bidirectional graph modeling have shown promising results in capturing user interactions, they face significant challenges in effectively leveraging bidirectional information and addressing dynamic user preferences. To overcome these limitations, we propose Bidirectional Transformer Graphs for Sequential-Social Recommendation (BiTG4Rec), a novel framework that integrates both supervised and self-supervised learning to analyze dynamic user preferences at sequential and social levels through bidirectional graphs. Our approach comprises three core components: 1) a Bidirectional Dynamic Graph Convolutional Network (BiDGCN) that models long-term sequential preferences, 2) a Bidirectional Dynamic Graph Attention Network (BiDGAT) that captures short-term sequential preferences, and 3) a Bidirectional Hyperbolic Graph Contrastive Learning module (BiHGCL) that extracts social preferences. To unify these diverse signals, we introduce a TransformerGated mechanism that dynamically integrates long-term, short-term, and social preferences. Extensive experiments on multiple real-world datasets demonstrate that BiTG4Rec consistently outperforms state-of-the-art methods, validating its effectiveness for sequential-social recommendation tasks.