Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems

Fei Long,Zhihan Lv

doi:10.1155/2020/7945417

Abstract

With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

Highlights

With the rapid development of the Internet and the increasing popularity of smart phones, the information that people can access was increasing in complex systems
The recommendation system came into being [1,2,3,4]. e introduction of complex context information brings more ideas to improve the efficiency of the recommendation algorithm. e main complex context information includes user context information, such as users’ ages, occupation, and region; physical context information, such as location, weather, and time. ere is much complex context information that could be considered by the recommendation algorithm. e context information can bring more accurate recommendations to the users of the recommendation system for complex real-world applications [5,6,7,8,9]
Few data can be obtained with relatively low data dimensions, but in the context of mobile information services, relying only on the “user-item” twodimensional recommendation model cannot generate a user for a given situational context effective personalized recommendations [11,12,13,14]. erefore, it is very important and urgent to provide personalized recommendations for mobile users in specific situations

Summary

Fei Long

Received June 2020; Revised July 2020; Accepted 28 July 2020; Published 5 October 2020. With the rapid development of information technology, the information overload has become a very serious problem in web information environment. Current recommending algorithms are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources realize the personalized recommendation. E experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources realize the personalized recommendation. e experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance

Introduction

Situation User

User B

Conclusion