Design And Implementation Of Drilling Platform Based On Hybrid Recommendation Algorithm

With the rapid development of the mobile Internet,we have entered an era of information explosion in the context of big data.The integration of the Internet with other industries is driving technological progress and social development,such as Internet + education,Internet + medicine,Internet + finance,etc.People are surrounded by a lot of data,and the problem of information overload appears.In order to reduce this trouble that the problem causes,the recommendation system came into being.The essence of the recommendation system is that when the user does not have a clear need,it searches for information that the user will be interested in from a large amount of information,which greatly improves the efficiency of information distribution and acquisition.Today,the globalization of the artificial intelligence industry is unstoppable,the IT industry has further developed,and the shortage of IT talent has become increasingly serious.Traditional classroom teaching has limitations in time,space,and educational resources.It can no longer train excellent workers for enterprises.Therefore,online learning has become increasingly popular and has become a new way for most students to enrich their skills.However,although many online learning platforms have broken the constraints of time and space conditions and provided rich learning resources,they have not personalized learning resources for different students and are not good at learning timely.In recent years,recommendation systems have been widely used in e-commerce websites,video websites,consulting and life service platforms,with significant effects.Applying a recommendation system to a learning website,paying attention to the learning habits and content tendencies of student users,and recommending different learning resources for different students will greatly improve the user experience and learning efficiency.In addition,social recommendation,as one of the most active areas in the research field of recommendation systems,has promoted the development process of personalized recommendation and has helped to increase user stickiness.Therefore,it is of great practical significance to combine social recommendation with traditional recommendation algorithms to realize a student drilling platform based on hybrid recommendation algorithms.In order to implement drilling platform based on hybrid recommendation algorithm,the main research contents of this paper are as follows:1.This paper introduces the research background and significance of the drilling platform,and then details recommendation system,content-based recommendation algorithm,collaborative filtering recommendation algorithm,and hybrid recommendation algorithm.It describes the cold-start problem and similarity calculation,and briefly describes the framework used in platform.2.This paper analyzes and outlines the requirements of the drilling platform in detail.Slope One algorithm in collaborative filtering and TrustSVD algorithm in social recommendation are selected to complete the design of hybrid recommendation algorithm model through data modeling and experiments.3.This paper applies the hybrid recommendation algorithm model to the platform,and implements a drilling platform based on the hybrid recommendation algorithm.The platform provides question bank and learning,competition world,digital library,recommended paper website,WIKI,discovery and exploration module,and personal center module for student users,provides contest management,teaching overview,selection of outstanding notes,question bank management,library management,and online Q&A modules for teacher users.Experiments and tests show that the platform functions are basically in line with expectations,and effective personalized recommendations can be made.