Research on educational resource recommendation system based on MRLG Rec

Internet information capacity is expanding and developing at a high speed every day. The huge amount of information data involves many problems, such as resources could be scattered, overlapped and confused and thus difficult for users to timely and accurately capture information suitable for them, especially in the field of education. Through the MRLG Rec algorithm, the following processes can be carried out: deep processing, characterisation of data to generate unique individual portraits of user information and to store education resource databases at the same time and analysis of recommendation system to provide users with more accurate high-quality education resources, thus forming a two-way unblocked and efficient information transmission closed-loop, which help learners find teaching resources of the time. It also has screening channels of high-quality educational resources. Through the analysis of experimental data, it is concluded that the algorithm improves the user viscosity of human-computer interaction in the teaching resource recommendation system.