Personalization recommendation system and method of network teaching resources

Abstract

The invention discloses a recommendation system and method of network teaching resources. The system comprises a data establishing module, an off-line data processing module and an on-line recommendation module. The data establishing module establishes teacher behavior data, teacher model data, a course model data and resource model data. The off-line data processing module is used for initializing and adjusting course model data and resource model data, teacher behavior data are used for deducing teacher identities, the degree of association between the resources is computed according to the teacher behavior data, the similarity between the resources is computed according to the resource model data, and the degree of association between the resources and courses is computed according to the resource model data and the course model data. The on-line recommendation module describes on-line recommendation resources through the association degree between the resources, the similarity between the resources, the association degree between the courses and the resources and the dynamic states of teachers, and the on-line recommendation module transmits teacher behavior data to the teacher behavior data of the data establishing module according to the feedback recommendation resource labels of the teachers on the recommendation resources through UI interaction.

Description

technical field [0001] The invention relates to the technical field of computer Internet, in particular to a personalized recommendation system and its implementation method in the aspect of networked teaching resources. Background technique [0002] With the rise of E-Learning, online teaching resources are also growing rapidly. The overload of information has brought many challenges to teaching organizers and learners. Therefore, the recommendation system, which shines in the commercial field, has gradually been applied to the education field. It uses the user's historical behavior data to perform personalized calculations, discover users' points of interest, and guide users to gradually discover demand information or Resources, which to a large extent, improve the user's work and study efficiency. [0003] The current popular recommendation algorithms include collaborative filtering recommendation (Collaborative filtering, referred to as CF), content-based recommendation...

AI Technical Summary

Problems solved by technology

In the formal teaching process of primary and secondary schools, teachers need to prepare a large amount of teaching materials and materials to prepare for teaching, and in the teaching of primary and secondary schools in China, there are problems such as regional differences in students' books and uneven levels of teachers.

At present, many recommendation systems have low recommendation accuracy, directly use collaborative filtering methods, lack of understanding of course content, resulting in outdated recommended resources, low correlation between recommendation results and prepared course tasks, and the system is in the initial operation. , the addition of new users or new resources, the lack of historical behavior records, so it is easy to cause problems such as sparse user historical behavior data, and cold start; ontology is based on the basic terms and their relationships in the vocabulary of specific domains, and the combination of these terms and relationships Defining the formal expression of vocabulary extension rules usually requires experts in this field to construct and maintain. Using an ontology structure in a recommendation system requires a lot of human and material resources. Therefore, a method that can be based on course content and can save Labor cost, a personalized recommendation system that recommends relevant teaching resources

Images