| Abstract |
With the rapid development of information technology and the widespread use of the Internet, intelligent recommendation systems have emerged, providing personalized information recommendation services to users by analyzing their historical behaviors, preferences, and psychological characteristics. Among them, recommendation systems based on user personality and interests have become a research hotspot. This paper first elaborates on the importance of user psychological characteristics in recommendation systems, then analyzes the specific characteristics of user personality and interests, and delves into the principles of recommendation systems based on these characteristics, including data collection and processing, user personality and interest modeling, design and implementation of recommendation algorithms, as well as the generation and optimization of recommendation results. The paper also introduces application cases of this system in various fields such as e-commerce platforms, social media, online music platforms, and online education platforms, and points out the challenges it faces, including data privacy protection, the cold start problem, the balance between diversity and novelty, as well as algorithm bias and fairness. Finally, the paper looks ahead to the future development directions of recommendation systems based on user personality and interests, including deep integration with psychological theories, cross-platform data integration and analysis, real-time and dynamic capabilities, intelligence and autonomy, as well as socialization and interactivity.
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