Research And Implementation Of Medicine Recommendation System For Online Pharmacies Oriented To Internet Healthcare

With the development of e-commerce technology and the increasing demand for medical and health services,online medicine trade is developing rapidly in China,and online medical service has become an irresistible trend.However,there are some shortcomings in online medicine trade,for example,most users lack professional knowledge of diseases and medicines,so the medicine often fails to suit the symptoms,and even leads to more serious illness by using the wrong medicines.Therefore,in order to solve this problem,this thesis proposes a collaborative filtering recommendation method based on expert system to build an intelligent medicine recommendation system.Expert system guarantees the professionalism of medicine recommendation,so as to ensure that medicine recommendation can be suit for the symptoms.The collaborative filtering method mainly considers the individual differences of users,and combines the two methods to ensure the professionalism and effectiveness of medicine recommendation.Firstly,this thesis introduces the situation of online health service at home and abroad,mainly introduces the development of online pharmacies,and introduces the bottlenecks and problems of online pharmacies,leading to the research direction of this article.Then,describe the research and development of expert system and personalized recommendation,and the objectives and contents of this study are clarified.Then,the related theories and techniques used in this thesis are introduced in detail,which lays a theoretical foundation for further research and implementation.Next,we focus on the design and implementation of intelligent medicine recommendation algorithm and system,including:(1)the design and implementation of expert system.In the construction of expert system,we use the production rules combined with XML knowledge representation to store expert knowledge,use D-S evidence synthesis theory to solve uncertain problem,and innovatively add cache design to expert system;(2)The design and implementation of collaborative filtering recommendation algorithm are introduced in detail,including data acquisition method,data processing method and final recommendation result generation algorithm.Data acquisition is mainly based on implicit feedback,supplemented by explicit feedback,and generation and revision of "user-drug" interest matrix.The implementation of collaborative filtering algorithm is discussed.The traditional similarity calculation method is improved to improve the accuracy of the algorithm,and the threshold judgment method is used to generate similar groups of target users,which reduces the time complexity of the algorithm and improves the efficiency of the algorithm;(3)The design and implementation of online pharmacy system,including demand analysis,overall design,database design of online pharmacy and implementation of related core modules.Finally,in order to verify the availability of the system and the improvement effect of the recommendation algorithm,relevant experiments are designed to test and verify the system and algorithm,and the test results are analyzed.Finally,the thesis is summarized and the shortcomings of this work are prospected and analyzed.The collaborative filtering recommendation method based on expert system proposed in this thesis is an effective method for online medical recommendation.Combining with the optimization method designed in this thesis,the accuracy and efficiency of online recommendation can be improved,and better medical and health services can be provided for users.