Personalized Recommendation System for Medical Assistance using Hybrid Filtering

Abstract

Recommender systems assist the consumers of service oriented environment to find out and select the most suitable services from a large number of available ones. Proposed paper is based on Personalized Recommendation System for medical assistance using keyword extraction. User can search doctor"s profiles or hospital names according to doctor and hospital attributes. Natural Language Processing (NLP) is used to process user"s ratings and reviews to compute system ratings. Depending on users rating and reviews, profiles are recommended. Medical-based Personalized Recommendation System computes similarity between given and collected attribute by using top-k query which is used to recommend each doctor profile and hospital name for each attribute in information retrieval. Personalized Recommendation system for medical assistance yields 0.06 satisfactions and 0.02 accuracy.