Hello everyone, I am Weixue AI. Today, I would like to introduce the practical application of knowledge graph 24 - a recommendation system for students' personalized courses based on py2neo. This project uses knowledge graph technology to provide a personalized online learning experience, and builds a The knowledge graph of hundreds of courses and learning resources generates personalized learning paths and recommended content for each student by analyzing the user's learning history, interests and abilities. Students can choose courses according to their own needs and interests, and the system will continuously adjust the recommended content to adapt to the students' learning progress and preferences, providing a better learning experience and effect. This personalized learning style can improve students' learning motivation and learning effect.
This article will introduce in detail the background of the knowledge graph in the student personalized course recommendation project, and how to build a knowledge graph containing hundreds of courses and learning resources. We will provide a complete data sample and import it into the knowledge graph based on py2neo, and then write the recommendation algorithm code.
Table of contents
- Background of the project
- Knowledge map construction
- Data samples and formats
- data import
- recommendation algorithm
- User portrait construction
- Recommendation Algorithm Implementation
- in conclusion
1. Project Background
In modern education, the recommendation of personalized learning paths has become an important requirement. Through a deep understanding of students' learning history, interests and abilities, we can construct a personalized learning path, thereby improving the efficiency and quality of learning.
As a tool that can represent and explore complex relationships, knowledge graphs provide the possibility to construct such personalized learning paths. By building a knowledge graph that contains hundreds of courses and learning resources, we can deeply understand the internal connections between courses, and then generate personalized learning paths and recommendations for each student by analyzing the user's learning history, interests and abilities content.