An AI-Powered Hybrid Recommender for Adaptive Government Service Allocation Using Machine Learning and Behavioral Filtering
Year: 2025
Type: article
Abstract: In India, citizens are supported throughout health, education, agriculture, employment, and social welfare through government services and welfare schemes. A lack of any centralized information and any digital literacy together with personalized guidance leaves many citizens unaware of those eligible services. In suggesting the suitable government services using the citizen’s profile and the past application history, this paper proposes a hybrid recommendation system in order to bridge this gap. Collaborative filtering joins content-based filtering that uses user choices plus demographics for correct suggestions. This analysis observes behavior among similar users. Accessibility, awareness, also trust in digital governance are improved when the system processes citizen data to provide a personalized list of services because citizens log in securely. The model aims to support India’s Digital India mission by enhancing public engagement and ensuring inclusive access to government services.
DOI: 10.64252/p0brnk04
Authors: Rahul M Andalageri, Prajwal S, Praveen Hirekudi, Ananya C, Raghavendra M, Dr. Samitha Khaiyum, Prof. Raksha Kodnad
Institution: Dayananda Sagar College of Engineering
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414097181