Review of Doctor Recommendation Methods and Applications for Consultation Platforms

This paper aims to strengthen the use of personalized recommendation technologies in online medical settings,help patients choose resources for high-quality physicians,and address the information overload caused by the growing volume of online consultations.Firstly,bibliometrics summarizes popular research directions.On this basis,this paper sorts out the existing online doctor recommendation methods and classifies them into five categories based on doctor-patient matching:recommendation based on traditional recommendation algorithms,recommendation based on multi-attribute decision making,recommendation based on machine learning,hybrid recommendation,and others.In addition,we compare the application status,advantages and disadvantages,and the application scope of each category.Finally,we analyze the trend of online doctor recommendations and propose future research directions.Online doctor recommendation belongs to the intersection of research problems in the fields of computer science,management,and medicine.In contrast to traditional recommender systems,online doctor recommendation prioritizes precise matching between patients' conditions and doctors' specialties.Traditional recommendation algorithms are initially applied in doctor recommendation,but they are constrained by data sparsity and cold start.Recommendation based on multi-attribute decision making possess a solid theoretical foundation and can flexibly reflect patient preferences,yet they require a high level of interaction between the system and patients.Recommendation based on machine learning can alleviate the challenge of data sparsity and enable intelligent recommendation,though they necessitate large data support and often suffer from poor interpre-tability.Hybrid recommendation models,by integrating the strengths of various algorithms,have the potential to improve recommendation performance.However,the challenge lies in combining and balancing these algorithms.Other research directions such as recommendation grounded in optimization theory and graph models remain to be explored.In the future,it will be important to integrate multidisciplinary methodologies,conduct research on cross-platform,multi-source,and heterogeneous doctor-patient data mining,expression,and integration,and explore doctor recommendation modes based on patients' needs and preferences.