Author: Zen and the Art of Computer Programming
Personalized Recommendation System Based on Knowledge Graph: Challenges and Opportunities
- introduction
1.1. Background introduction
A personalized recommendation system is a recommendation system that uses information such as user history, interests, and preferences to recommend suitable products, services, and other content to users through machine learning, deep learning, and other technologies. Knowledge graph is one of the core technologies to realize personalized recommendation system. By modeling entities, attributes and relationships, it realizes efficient storage, management and reasoning of data, and provides the underlying support for personalized recommendation.
1.2. Purpose of the article
This article aims to introduce a personalized recommendation system based on knowledge graphs, explain its challenges and opportunities, and give relevant technical implementation and application cases in practice.
1.3. Target Audience
This article is mainly for readers with a certain technical foundation and interest, especially focusing on the technical development, challenges and future trends in the field of personalized recommendation systems.
- Technical Principles and Concepts
2.1. Explanation of basic concepts
2.1.1. Personalized recommendation system: Refers to a recommendation system that recommends suitable products, services, and other content for users based on information such as user historical behavior, interests, and preferences, and through machine learning, deep learning, and other technologies.
2.1.2. Knowledge graph: refers to the modeling of entities, attributes and relationships to achieve the underlying support for efficient storage, management and reasoning of data.
2.1.3. User portrait: refers to the modeling of user behavior, interests, preferences and other information so that the personalized recommendation system can conduct user portrait analysis and behavior prediction.
2.1.4. Collaborative filtering: refers to the analysis of user historical behavior, interests, preferences and other information to find similarities between users and other users, so as to recommend appropriate content for users.
2.2. Introduction to technical principles: algorithm principles, operation steps, mathematical formulas, etc.
2.2.1. Collaborative filtering algorithm: By analyzing information such as user historical behavior, interests, preferences, etc., it is found that the relationship between the user and other users