Research On Recommendation Of Online Education Resources Based On Trust Relation Assistance

When studying on online education platform,too many learning resources may lead some problems for learners,such as learning lost,knowledge overload.In order to provide learners with better learning services,personalized learning resources recommendation is an important solution to these two problems.In recent years,recommendation system has penetrated into many information industries area,such as e-commerce,news and so on.There are many similarities between recommendation of learning resources and recommendation of news and commodities,but recommendation in education area has its own unique limits and needs.Practice in other recommendation areas has proved that social relationships among users can improve the recommendation effect,while online education platform also provides user interaction scenarios such as discussion community.Based on the above considerations,this paper will research and implement the recommendation method of online educational resources based on trust relationship assistance.Specifically,this research will be carried out in the following three methods: online education platform trust modeling method,basic education resource recommendation method and trust relationship-based recommendation method.Trust modeling of online education platform is the research foundation of this paper,which supports the design and implementation of trust-based recommendation.In order to solve three essential problems in trust modeling on online education platform: trust relationship extraction,trust network construction and trust network expansion,this paper defines two-dimensional trust opinions consisting of trust value and reliability value,and defines the basic operations of aggregation and expansion of trust opinions based on ring theory.Then we design and optimizes trust modeling algorithm utilizing the properties of ring and establish interpretable and informative trust networks.This paper also studies collaborative filtering recommendation methods of learning resources,because of the limitation of trust-based recommendation.Trust-based recommendation only focuses on social behavior,which makes it not able to make full use of personalized information of resources and users,such as learning interest,knowledge structure.In this paper,we carry out experiments on anonymised data and xuetangx datasets.There are three experiments on basic recommendation methods: memory-based collaborative filtering,logistic regression and factorization machine.When recommending by logistic regression and factorization machine,we use the other project teammates' research results on user portraits,and add user learning attitude tags a to the input feature vector.By comparing and analyzing the accuracy and recall rates,we finally choose FM as the basic learning resources recommendation method which gets the best performanceUtilizing the results of trust modeling,this paper designs and implements a trustbaesd recommendation algorithm based on collaborative filtering which integrates trustbased recommendation with user-based collaborative filtering recommendation.We carry out experiments on Epinions dataset.The results verify the value of the proposed trust modeling method for recommendation,and prove that the hybrid recommendation method is superior to the single recommendation method.But a hybrid recommendation model based on factorization machine is finally proposed,which integrates the basic attributes,personalized labels,trust relationship,learning ability and other characteristics in user vector.What is more,we refer to the industrial recommendation architecture and design an online learning resources recommendation system,which merges plenty of recommendation methods.