Design Of Recommendation Algorithm And Medical Expert Recommendation Platform Based On Python

The continuous development of network information technology has promoted the continuous improvement of resource sharing and information acquisition needs.With the continuous improvement of human living standard,the acquisition and application of medical information has attracted more and more attention.How to quickly find the target information in the mass of medical information is an urgent problem to be studied.Traditional manual search of medical expert information is often inefficient and incomplete.According to people's demand for medical expert information,this paper studies the medical expert recommendation platform and information recommendation algorithm based on Python by using computer and information processing technology and network sharing data technology,so as to provide users with a convenient and effective platform for medical information inquiry and acquisition,which has a broad application prospect.Main work of the master dissertation: based on the comprehensive review of the recommendation needs and algorithms of medical experts,the dissertation studied the technical issues such as the recommendation platform of medical experts and the design of the recommendation algorithm,including: the thesis summarized the research background and significance of the recommendation platform of medical experts and the current situation at home and abroad;This paper briefly describes the platform design technology,analyzes the general needs of the current medical expert recommendation platform,and analyzes the advantages and disadvantages of the basic recommendation algorithm.Django framework and C/S mode are used to design the platform.By using Web crawler technology,medical expert data can be obtained from specific network,and data cleaning technology can be used to remove dirty data and ensure the content of information.The principle of collaborative filtering recommendation and tag-based recommendation algorithm is analyzed in detail.According to the characteristics of the algorithm,the collaborative filtering and tag combination recommendation algorithm is proposed.The similarity optimization calculation is carried out by using the tag information,and the scoring matrix is predicted according to the optimized similarity to obtain the recommendation result.Finally,the prototype of medical expert recommendation platform was designed and realized.The results of medical expert recommendation were analyzed and evaluated through the prototype system,so as to verify the effectiveness and feasibility of collaborative filter-tag combination recommendation algorithm.The prototype test shows that the recommendation effect can reach the expected target.The proposed collaborative filtering and tag combination recommendation algorithm can be applied to the medical expert recommendation platform to effectively deal with the problemof cold start and data sparsity in the traditional algorithm and improve the accuracy of the recommendation results.