Health Insurance Recommendation using Probabilistic Data Structures

Recommender systems are constantly evolving in the health insurance industry, with a focus on speed and efficiency. To meet the demands of clients, insurance providers need to generate personalized policy recommendations that are quick and accurate. This paper suggests a health insurance recommendation system that combines advanced algorithms to offer efficient and personalized recommendations based on user preferences and similarities. The proposed system leverages the MinHash and LSHForest algorithms, as well as collaborative filtering, to improve the recommendation process. MinHash approximates the Jaccard similarity between policies, while LSHForest enables faster search for similar policies. Collaborative filtering considers user preferences and similarities among policies and users. To ensure user input is secure and legitimate, the system employs a Bloom filter. This filters out unauthorized access to sensitive healthcare data and validates user input. The architecture of the system addresses various challenges related to personalization and privacy in health insurance recommendation systems. Based on the results, the proposed system surpasses existing approaches in accuracy, efficiency, and privacy preservation. This has the potential to be advantageous for both insurance providers and users as it helps streamline the process of choosing appropriate health insurance plans that align with individual requirements and preferences.