Personalized recommendation method with interest guidance function

Abstract

The invention relates to a personalized recommendation method with an interest guidance function. The invention discloses a digital learning path recommendation method. The method has the interest guidance function, and includes the following steps of: S1, constructing a knowledge interest guiding graph including knowledge layers and an interest-related phenomenon layer; S2, acquiring personal characteristics of a learner; S3, marking learned knowledge points, target knowledge points and phenomenon nodes; S4, constructing optimal learning paths from the target knowledge points to the marked phenomenon nodes; and S5, recommending the learning paths. According to the method, the interest guidance problem of target knowledge point learning of the learner, especially a child, is effectively solved, the learning difficulty is reduced, and the digital learning ability of the learner, especially the child, is enhanced.

Description

technical field [0001] The invention belongs to the field of digital learning, and in particular relates to a personalized recommendation method with an interest guiding function. Background technique [0002] With the development of mobile Internet, Internet-based digital learning provides users with a personalized learning environment. Personalized recommendation based on user interest is a common method, as reported in patent [201210178807.5], a personalized network learning resource recommendation method based on user interest, including analyzing learners' access to the network learning system based on extended topic graph According to the behavior data of individual learners and their groups, the learning interest path change pattern of the learning content is obtained, and then according to the learning interest path change pattern and the sequential relationship between the learning objects of the expanded theme map, the initiative is given to learners. Personalized...

AI Technical Summary

Problems solved by technology

[0004] However, for learning groups such as children, there are two problems in the existing recommendation methods: 1) From the perspective of education, comprehensive knowledge learning is required, and the interest bias expressed by children cannot be used as the criterion for learning. Moreover, children's interest points are easily d

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