Design And Implementation Of Online Education Course Recommendation System Based On Path Factor Fusion

With the continuous development of information technology,online education uses advanced technical means to move classes from offline to online,which overcomes the requirements and restrictions of time and geography in the traditional education model,and at the same time makes it more convenient for users to get educational resources.Especially during the 2020 epidemic,online education has become an important supplement to traditional education.However,with the continuous multiplication of Internet teaching resources,users are facing new problems and confusions in the selection of online courses.First of all,learning has the characteristics of path,and relevant courses must be studied according to the knowledge path,otherwise the wrong course sequence will seriously affect the learning effect;secondly,fragmented learning will lead to a broken course system,which is not conducive to users building correct knowledge structure.Traditional recommendation systems are always used in application environments with non-path factors,such as product recommendation.Especially the traditional collaborative filtering algorithm,its recommendation principle does not reflect the path of course learning.Therefore,this paper proposes a collaborative filtering recommendation algorithm based on path factor fusion.Based on the comprehensive consideration of the course order of most users,the algorithm defines the weights of adjacent courses and proposes a new course similarity calculation method.Theoretical analysis and experimental results prove that compared with the traditional recommendation algorithm,the recommendation algorithm has higher accuracy,and the recommendation result is more in line with the purpose of the learning path.The main work of this paper is as follows:(1)Proposing the concept of the whole path of backbone learning.According to the learning data of the historical user set,the concept of path weight of adjacent courses is put forward innovatively,which is processed by statistical method and depict the whole path of trunk learning.(2)Proposing a collaborative filtering algorithm based on path factor fusion.On the basis of the main learning path,the path factors such as the weight of the adjacent course path are integrated with the traditional project-based collaborative filtering algorithm.Measuring the weight of the adjacent course path and improving the calculation of similarity.Finally,the recommended course is more in line with the expected direction,which not only contains the user’s interest,but also reflects the path that the course should have.(3)Achieving the design and development of online education course recommendation system.The requirements of the recommendation system,the design of the system framework,and the realization of functional modules are analyzed.The development of the program of this system platform uses Python,and the design of the database table uses MySQL.