Design And Implementation Of Personalized Learning Resource Recommendation System Based On User's Interest Preference

In the 21st century,personalized learning as one of the basic characteristics of learning is a new trend in the "Internet+" era to promote the development of students'individuality.With the expansion of internet users and the need for building a learning society,many universities have established their own network online teaching platforms and shared high-quality curriculum resources and teaching resources.Especially,massive online learning platforms have sprung up with the rapid development of network technology including.However,some e-learning platforms are pursuing the number of learning resources and the amount of users' visits.They pay more attention to about the aggregation of information resources on the platform.As a result of that,the learners lose massive information resources,Thereby,they cannot obtain effective resources that meet their individual needs.Based on this development scenario,a large number of researchers has begun to combine online learning with personalized recommendation technology.At present,the recommendation technology has been widely used in many fields in which the most typical is the field of E-commerce.It has made a lot of achievements and progress.However,the related research of the recommendation technology in the fields of education is lacking in price.The key issues in data recommendation such as cold start and data scarcity have been the current research hotspots.This article focuses on the development trends of computer internet technology,the future needs of personalized learning,changes in education environment,and proposes an improved collaborative filtering recommendation method to achieve personalized learning resources recommendation.Specific studies include the following aspects:(1)Summarized the working principle of current popular recommendation technology,and analyzed the advantages and disadvantages.Focus on the characteristics of collaborative filtering recommendation technology,and applied this technology to the system designed in this paper.(2)Proposed a collaborative filtering recommendation method based on user's interest preference.The user's interest preference features were collected through data such as user interest tags,learning records,project basic attributes,and learning behaviors;Interest preference features were converted into corresponding preference feature values,and a user interest preference model was established based on these feature values;The similarity calculation and resource recommendation were performed based on the interest preference feature model and resource score.(3)Designed and developed personalized learning resource recommendation system by using the recommended method of this paper,which can effectively improve the recommendation effect.The main modules of the system include learning module,recommendation module,curriculum management module,which can realize personalized recommendation of online resources.