Research And Implementation Of Learning Resource Recommendation System Based On Bayesian Personalized Ranking

With the rapid development supported by Internet technology and the largescale implementation of intelligent applications,all walks of life have also carried out online operation mode.Education industry,as an indispensable part of national life,has been in a booming stage of rise.Compared with the traditional education industry,the online education based on information technology is in the ascendance.It breaks the time-space restriction and personnel barrier,makes full use of professional background such as visualization,intelligence and management to integrate resources with teaching,improve the interaction efficiency between learners and teachers,and realize the self-control and supervision of learners at each learning stage.Promote the development of online education industry.In the existing learning resource system,although part of the needs of learners have been met,with the exponential growth of resources in the information age,learners’ desire for the improvement of their own ability and other multiple factors,learners appear selectivity and confusion difficulties in obtaining learning resources,indicating that the feedback no longer meets the needs.In addition,under the existing education recommendation mechanism,there is no specific learning plan to recommend learning resources according to learners’ differentiated basis and mastery level due to the fragmentation of knowledge.In the knowledge era where everyone is both a learner and a communicator,the existing learning resource system only pays attention to the learners themselves,and does not input the knowledge transmission of learning resources into a closed loop.Based on this,this paper focuses on the development prospect of education industry in the information age,the practical needs of personalized resource learning and the changes of learners’ learning preferences,proposes a learning resource recommendation method based on Bayesian personalized ranking and carries out related system implementation.Specific research includes the following aspects:(1)Research and analysis of the current working principle of personalized recommendation technology and its application in the field of education and learning,through improving the existing technology to better apply to the learning resources recommendation system in this paper.(2)A recommendation method based on Bayesian personalized ranking is proposed to construct learner preference characteristics through learning behavior data such as learner learning record,interest label and behavior interaction.Professional clustering reinforcement learning is introduced to deal with learners’ cold start problem and paired preference Bayesian personalized ranking is improved based on learner interest cycle changes so as to carry out learning resource preference recommendation.(3)Design and develop a learning resource recommendation system based on learner individuation.Java language and other technologies are used to develop learner modules,resource recommendation modules,teacher modules and other submodules.The overall function is complete and the structure is reasonable,which can realize personalized recommendation and various management functions of learning resources and achieve the expected goals.