Research And Implementation Of Learning Resource Ecommendation System Based On Collaborative Filtering Algorithm

With the rapid development of Internet-related fields,the number of online learning resources increases exponentially every year.When facing the huge amount of online learning resources,learners have "information overload" and other issues.Recommendation system is dedicated to providing users with personalized and accurate information resources.At present,most of the recommendation systems are based on collaborative filtering algorithm,but most of the research in the user-resource scoring data sparse,recommendation accuracy is not high,and there are problems such as can not reflect the user's interest changes in a timely manner.For this purpose,the following work has been carried out in this thesis.(1)In order to improve the accuracy of user similarity calculation in collaborative filtering recommendation algorithm,this thesis combines item attributes with user ratings to solve the problem of low accuracy when user-resource ratings are sparse.(2)Aiming at the feature that people's interest will change with time,this thesis uses the Hermann Ebbinghaus forgetting model to reduce the order of the historical score data,and describes the change of user's interest in the collaborative filtering recommendation algorithm.(3)Through group recommendation technology,let teachers participate in the recommendation of learning resources,realize group recommendation of learning resources,make the recommendation of learning resources more humanized.(4)A personalized learning resource recommendation strategy based on improved collaborative filtering recommendation algorithm is proposed,and a prototype of learning resource recommendation system is constructed.The feasibility and validity of the system are verified by testing and applying the data of Yunnan "He Campus" platform.Finally,this thesis will build a learning resources recommendation system,which will be applied to the learning resources recommendation function module of Yunnan "He Campus" platform,and recommend personalized learning resources to learners in K12 stage to meet the personalized needs of users of Yunnan "He Campus" platform for learning resources.