Research And Implementation Of Personalized Medical Information Recommendation System

With the Internet era coming, network information has shown a trend of exponential growth. Medical information as part of network information has also shown a trend of exponential growth, and users are difficult to quickly find useful information they need which is the state reflected in the so-called "information explosion" and "information overload" internet era. A technical expert of Microsoft Research Asia responsible for the search said, "The general search engine does not come out 75% of the contents". Moreover, the general search engines often returns dozens of pages to users, but the users cannot read one by one to confirm whether the information is they need, this leads to the information that the user really requires is behind dozens or even hundreds of pages and it is not recommended to the user. Although general search engines can easily help us find mass of information, we hard to find that we really need. In order to improve the shortcomings of general search engines, this paper researches and designs a personalized recommendation system in the field of medical information, which can recommend related information to users and meet the needs of users well.This paper being based on data mining and information recommendation algorithm designs and implements a set of personalized medical information recommendation system for the special medical areas. Firstly, the key related technologies in this system are discussed to details, including the construction of user interest model and information recommendation algorithm and then analyze the advantages and disadvantages of several algorithms. Secondly, the design of personalized information recommendation system is elaborated detailed. Starting from system requirements, we build the overall framework of this system and design the user interest model, Chinese word segmentation module, the information pre-processing module, information recommendation module and personalized page customization module. Finally, the personalized medical information recommendation system has been implemented and then analyzes the test results. The results indicate that under laboratory environment the recommendation system can not only recommend the required information to users but also can recommend some relevant medical information to them.