An Internet Medical Service Recommendation Method based on Collaborative Filtering

The recommendation system could mine the user's behavior operation data and provide different personalized recommendation services for different users. The problems of inaccurate and incomplete description of patients' needs in internet medical service have brought great challenges to the recommendation of internet medical service. This paper takes the medical service field as the research object to improve the collaborative filtering algorithm in the recommendation system. Firstly, the static evaluation model based on doctor entity and hospital entity is established by using Analytic Hierarchy Process (AHP) model to realize the initial distribution of weight. Then, based on the evaluation methods of doctors and hospitals, the user interest model is established and K-means clustering is carried out for users, and dynamic recommendation is carried out to users by combining collaborative filtering recommendation method. The experimental results show that the proposed collaborative filtering recommendation model based on user interest clustering has smaller error, better recommendation effect and more accurate recommendation.