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@article{169473, author = {Siddhant Deshmukh and Shubham Godambe and Akanksha Shevkari and Tanavi Narkhede and Prof. S. S. Gadekar}, title = {Connecting daily wage workers to providers}, journal = {International Journal of Innovative Research in Technology}, year = {2024}, volume = {11}, number = {6}, pages = {1079-1084}, issn = {2349-6002}, url = {https://ijirt.org/article?manuscript=169473}, abstract = {This project presents a comprehensive job-matching platform designed to address the employment challenges faced by daily wage workers, particularly those with limited access to smart phones and digital services. By integrating technologies such as USSD (Unstructured Supplementary Service Data) and IVR (Interactive Voice Response) for communication , the platform ensures accessibility for non-smart phone users while offering multilingual support and geolocation-based services to enhance user experience. Machine learning algorithms, including K-Nearest Neighbors (KNN) for job matching and Content-Based Filtering for personalized recommendations, are leveraged to optimize worker-job provider connections in real-time. The platform was developed through a structured approach, involving a review of relevant literature, database creation, algorithm development, and rigorous testing. Results indicate improved accessibility, efficient job- worker matching, and high user satisfaction. This solution not only addresses the immediate needs of daily wage workers and job providers but also sets the stage for future advancements, contributing to greater inclusivity and societal impact in the labor market.}, keywords = {geolocation, real-time services, USSD, IVR, K-Nearest Neighbors, Content-Based Filtering}, month = {November}, }
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