ZoopZoop: An Integrated Personalized Recommendation and Notification System for Public Policy Information Services
Downloads
Public policy information is often distributed across multiple government institutions and service platforms, making it difficult for citizens to identify policies that match their individual circumstances, interests, and application schedules. Conventional keyword-based policy search requires users to actively identify relevant policy terms and repeatedly monitor policy information, which may result in missed opportunities and inefficient information exploration. To address these limitations, this paper presents ZoopZoop, an integrated personalized public policy information service that combines policy search, personalized recommendation, policy management, deadline notification, and conversational AI assistance within a unified web-based platform. The proposed system collects public policy information through government public data APIs and periodically synchronizes policy lists, detailed information, and eligibility conditions. Personalized recommendations are generated using two complementary sources of information: user behavioral information, including recent search and policy-view histories, and user profile information, including age, income range, residential area, employment status, gender, household size, and marital status. The system further provides notifications for approaching deadlines, newly registered policies, and recommended policies. To support natural-language interaction, an AI chatbot retrieves relevant policy information from the internal policy database and uses the retrieved information as contextual input for response generation. The system was implemented using React, Spring Boot, PostgreSQL, JWT-based authentication, and external public data and AI APIs. The implemented prototype demonstrates how personalized recommendation, proactive notification, and conversational interaction can be integrated into a unified public policy information service. The proposed system provides a practical foundation for improving the accessibility and personalization of digital public policy information services.
Public Data Portal, “Government24 Public Service Open API.” [Online]. Available: https://www.data.go.kr/data/15113968/openapi.do#/tab_layer_detail_function
Spring, “Spring Boot Reference Documentation.” [Online]. Available: https://docs.spring.io/spring-boot/
Spring, “Spring Security Reference Documentation.” [Online]. Available: https://docs.spring.io/spring-security/
Meta Open Source, “React Documentation.” [Online]. Available: https://react.dev/
OpenAI, “OpenAI API Documentation.” [Online]. Available: https://platform.openai.com/docs/
PostgreSQL Global Development Group, “PostgreSQL Documentation.” [Online]. Available: https://www.postgresql.org/docs/
Y.-J. Ahn, H.-Y. Cho, and S.-J. Kang, “Customized Recipe Recommendation System Implemented in the Form of a Chatbot,” Journal of the Korea Academia-Industrial Cooperation Society, vol. 21, no. 5, pp. 543-550, 2020.
J.-H. Jung, W.-J. Lee, H.-W. Choi, D.-Y. Seo, J.-H. Lee, H.-B. Park, and H.-Y. Lee, “Development of a Customized Webtoon Recommendation Platform Using the GPT Model,” Proceedings of KIIT Conference, pp. 1361-1364, 2025.
J.-W. Son, H.-B. Kim, D. Yoon, S.-H. Park, K.-I. Lee, and S.-J. Kim, “Development of an AI Chatbot-Based Personalized Restaurant Recommendation System,” Proceedings of KIIT Conference, pp. 1273-1277, 2025.
S.-R. Jung and J. Kim, “Sentiment-Fused Hybrid Recommender System for Movie Domain: Integrating Aspect-Based Emotion Analysis with Collaborative-Content Filtering,” Journal of the Korea Big Data Society, vol. 10, no. 1, pp. 209-219, 2025.
