Research And System Implementation Of Monte Carlo Based News Recommendation System

According to data age 2025,a report released by the Internet data center,the total amount of data produced in the world has reached 33ZB in 2018,and Internet data is in an explosive growth stage.While enjoying the benefits of information and resource sharing brought by the information society,people also have to "painfully" look for the "trickle stream" they are interested in in the massive data of the Internet,so the demand for fast and accurate access to personalized information services is also growing.At the same time,people are more likely to use fragmented time,more passive access to information.However,traditional methods such as using search engines and viewing information portals are not enough to meet the above demands,which also makes the more intelligent recommendation system favored by people.There are two major difficulties in the design of practical recommendation system.First,the real recommendation system is often faced with the calculation of preference between massive users and items.Online data needs to be quickly "digested",which requires high algorithm timeliness and data processing capacity.Then,the user preference needs to be combined with data analysis from multiple sources.It is difficult to make effective use of multi-source data and realize complementary defects on the basis of ensuring algorithm performance.In our work,according to the characteristics of news recommendation data,a mixed recommendation model was designed to describe user preferences from multiple perspectives,and the evolution process of user interest over time was analyzed with the interest model,so as to improve the hit rate of recommendation.Using the monte carlo method,the recall strategy of the recommended algorithm is designed,which avoids the problem of the algorithm's time cost caused by the global calculation and ensures the availability of the algorithm.Using monte carlo strategy to optimize the performance of recommendation system is a meaningful attempt in the field of recommendation system.It is believed that with the increasing of the data scale and data sources used by personalized recommendation service,more solutions to the problem of algorithm cost,such as monte carlo,will emerge.This practice will also bring enlightenment to the subsequent research and solution of related problems,which has certain reference significance.