An AI-Powered Hybrid Recommender for Adaptive Government Service Allocation Using Machine Learning and Behavioral Filtering

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.