Research On Health Knowledge Sharing Mode And Personalized Recommendation Algorithm

With the improvement of people's material living standards,people are paying more and more attention to their own health,and they tend to search the internet for knowledge about health.In the information society,the amount of information and data in the Internet is increasing day by day.It is a very tricky thing to filter out such a huge amount of information that meets the needs of users.Classified list websites and search engines were born to solve the above problems.However,these two methods have limitations.The classified list website and the search engine are limited to the user's own known range and will not be personalized for the user's needs.In order to overcome the above problems,various Internet communities provide users with faster and better services by collecting user behavior information and using personalized information recommendation algorithms.However,in the field of health knowledge sharing,the problem of choosing a personalized recommendation algorithm and migrating the algorithm to the actual system still needs further study.This article aimed to provide users with health knowledge sharing services and personalized information recommendation services,mainly from the following aspects:1.In response to the low enthusiasm of users to participate in health knowledge sharing,this paper designed a health knowledge sharing interlocution model based on a mixed incentive mechanism.This article analyzed the motives of people for knowledge sharing and common knowledge sharing patterns in the Internet,such as community forum mode,log mode,upload/download document mode,and question and answer mode.2.For the traditional recommendation algorithm can not timely sense the user's interest changes,this article designed a collaborative filtering algorithm based on the natural genetic law curve,and applied it in health knowledge personalized recommendation service system.The algorithm used an forgetting function to attenuate the user's interest and model the user's interest.Then,in combination with the collaborative filtering algorithm,the user's nearest neighbor set is found out and the user is recommended for the item of interest.Experimental test results showed that the precision and recall rate of this personalized information recommendation algorithm has reached the requirements for initial use.3.Based on the above research,this paper designed and implemented a personalized user-professional health knowledge quiz recommendation system based on the Android platform.The system established an interactive platform between ordinary users and experts,and analyzed the user's behavior data through a personalized recommendation algorithm to recommend content that users are interested in.