Online Doctor Recommendation Method for Doctor-Patient Interaction Data and Doctor Attributes

At present, LDA topic model, Word2Vec model and knowledge graph have been used to recommend doctors for online consultation, and have achieved certain results. However, the single word vector model will lead to some deficiencies in the recommendation results. In online consultation, patients have short questions and doctors have long answers, which is difficult to deal with uniformly. Therefore, this paper combines Sentence BERT sentence vector model, ELMO word vector model and doctor attribute model to build a set of combined recommendation model for intelligent doctor recommendation. In the empirical study, this paper chooses a large number of test patients as the research object. The results show that compared with the single word vector model, the combined recommendation model proposed in this paper is better in the accuracy rate, mean reciprocal rank and coverage rate of doctor recommendation.