Study on information personalized recommendation based on system dynamics

Abstract

With the popularity of mobile intelligent terminals such as mobile phones in the whole society,the production capacity of digital content has sunk to all levels of the society,forming a multi-source,independent and native Internet media content manufacturing pattern.With the vigorous development of various emerging media represented by social media and we media,the propagation ability of digital content has been greatly enhanced,especially in the reporting of sensitive,hot and important events in the process of propagation,which will produce a lot of derivative content.The improvement of the above two abilities causes the internet information to be characterized by mass,uneven content quality and multi-point of view.How to accurately recommend the news of correct value orientation and accurate information disclosure related to judicial work to maintain and promote social fairness and justice has become a new problem and challenge in the judicial field. 河北科技大学学报 2021年 第2期 王子岩，等:基于系统动力学的资讯个性化推荐研究 Recommender system effectively solved the problem that it was difficult for users to find the information they need efficiently in the mass of information.Content based recommendation technology analyzed the items that users are interested in before,got the similar items by calculation,and then pushed the items with the highest similarity to users.Collaborative filtering (CF) is the most widely used recommendation system,which was first proposed by Goldberg in 1992 when developing tapestry e-mail filtering system.Its core idea is to analyze the user′s historical behavior data through the algorithm,mine the user′s interest preferences,classify users according to different interest preferences,and recommend items with similar preferences.Collaborative filtering is the most widely used algorithm in recommendation system.It was first proposed by Goldberg in 1992 when developing tapestry e-mail filtering system.Its core idea was to analyze the user′s historical behavior data through the algorithm,mine the user′s interest preferences,classify users according to different interest preferences,and recommend items with similar preferences.At present,personalized recommendation has been widely used in e-commerce,film and television works,food and beverage,news and other fields.For example,the recommendation of Jingdong started in 2012,and the recommendation products were based on rule matching.The combination of the whole recommendation product lines was like a loose primitive tribe,and there was no intersection of engineering and algorithm between the tribes.Taobao launched the recommendation engine of "personalized recommendation",namely "thousands of people and thousands of faces" in 2013,which used some users′behaviors to speculate what users may like through algorithms.Meituan has built the world′s largest food knowledge base,created knowledge graphs for more than 2 million businesses and 300 million products,made user portraits for 250 million users,and built the world′s largest O2O intelligent recommendation platform for users. Douban used social behavior analysis to solve recommendation problems,such as collaborative filtering technology based on the sameusers′behavior ,and friends or neighbors recommendation,etc.,which is also a supplement of personalized recommendation.The introduction of social recommendation can solve the problem of narrow recommendation range caused by simple product content recommendation.The personalized recommendation algorithm of Toutiao was based on voting,and its core idea was to vote.Each user can cast his only vote to the article he likes.After statistics,the final result was likely to be the best article in this crowd,and the article would be recommended to the same group of users.This method seems to be very simple,but in fact,it needs massive user behavior data mining and analysis.System dynamics is an interdisciplinary subject based on system theory,cybernetics and information theory,and with the help of computer simulation technology.From the perspective of system,structured and dynamic analysis and model simulation are conducted,which is good at analyzing high-order,nonlinear and time-varying complex systems,and is suitable for analyzing the dynamic and complex process of personalized information recommendation by combining qualitative and quantitative analysis..Based on the theory of system dynamics,this paper modeled and simulated the important factors that affect the effect of information recommendation in Vensim software,and constructed the causal feedback model and stock flow model including the number of users,articles,tags and the influence among subsystems.The system dynamics equation model was established,and the sensitivity analysis of related factors was carried out.The results show that the number of articles,the characteristic tags and the interest factors of articles all have an important impact on the recommendation effect.They are the key factors to be considered in the design of the recommendation system,and are also the important ways to solve the key problems,such as the cold start,real-time and "information cocoon room" of the recommendation system.Research on information personalized recommendation based on system dynamics can actively and effectively meet the challenges of information disclosure in the judicial field and improve the accurate recommendation effect.

Keywords

• data processing; personalized recommendation; judicial work; system dynamics; information platform; simulation