User Preference Prediction & Personalized Recommendation based on Item Dependency Map

IDM을 기반으로 한 사용자 프로파일 예측 및 개인화 추천 기법

In this paper, we intend to find user's TV program choosing pattern and, recommend programs that he/she wants. So we suggest item dependency map which express relation between chosen program. Using an algorithm that we suggest, we can recommend an program, which a user has not saw yet but maybe is likely to interested in. Item dependency map is used as patterns for association in hopfield network so we can extract users global program choosing pattern only using users partial information. Hopfield network can extract global information from sub-information. Our algorithm can predict user's inclination and recommend an user necessary information.