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@article{180662,
        author = {Siddhant Deshmukh and Shubham Godambe and Tanavi Narkhede and Akanksha Shevkari and Prof. S. S. Gadekar},
        title = {LabourHub: A Smart Platform for Daily Wage Job Matching Using ML and USSD Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1947-1951},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180662},
        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)  
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, , K Nearest Neighbors,Content-Based Filtering},
        month = {June},
        }