A Study On Knowledge Recommender Algorithm Based On Time Transition

With the rapid development of the Internet,information transmission becomes more convenient,however information overload becomes more and more serious.Rcommendation system effectively alleviates the information overload problem,it can recommend the user's personalized interest items according to the user characteristics and item characteristics.So far,recommendation system has been widely applied to many fields.However in the field of online learning,it needs more exploration.Time transition as one of the most important factors of knowledge recommendation,its influence model needs further research and experiment.Based on the analysis of the characteristics of knowledge recommendation and the influence of time transition in knowledge recommendation,this paper proposes a model of time transition under two different learning modes: short-term learning and long-term learning,and proposes a time transition based knowledge recommendation algorithm?This paper uses Tianjin University Online Judge as recommendation platform,builds models of user and item,and proposes a time transition based knowledge recommendation algorithm for online judge.Meanwhile,this paper uses MovieLens dataset and Tianjin University Online Judge data for experiment,and form a knowledge recommendation algorithm that can adjust different online learning platform,thought analysing the experiment results.