Research on online medical community doctor recommendation based on information fusion

Abstract

At present, more and more methods are used to solve the problem of doctor recommendation in online medical community. LDA topic model, vector space model, AHP method, knowledge map and other methods have shown good characteristics and recommendation effect. However, different recommendation methods often get different results, which often leads to inconsistent recommendation, so there are some deficiencies. This paper introduces information fusion technology to fuse the recommendation results obtained by different recommendation methods, obtains further optimization results, and solves the problem of inconsistent results obtained by different recommendation methods. Based on LDA model and word2vec model, this paper established a doctor recommendation model based on information fusion. In the empirical research, Haodaifu (www.haodf.com) has been selected as the research object. The empirical results show that the doctor recommendation model based on information fusion is better than the separate LDA topic model and word2vec model in accuracy and effectiveness.