Author(s)

Hetong Wang

Corresponding Author

Hetong Wang

Abstract

This paper investigates precision marketing models driven by content recommendation and user-interest matching, using the short-video platform Douyin (TikTok in China) as a case study. In the digital era, algorithm-driven content recommendations have transformed traditional information retrieval into proactive, personalized content distribution, significantly enhancing marketing efficiency. Douyin employs a "two-tower" recommendation framework combined with deep learning algorithms, effectively capturing user interests and content features, substantially increasing user engagement. Through analysis of user behavior data, the study reveals that algorithmic recommendations enable precise user segmentation, dramatically improving marketing conversion rates and user loyalty. However, excessive reliance on recommendation algorithms can also cause user fatigue, information redundancy, and privacy concerns. Therefore, the paper emphasizes achieving a balance between algorithmic efficiency and user experience, advocating moderate personalization and suggesting human-algorithm collaboration to enhance content novelty and diversity. Future precision marketing should prioritize user needs and privacy protection, integrating technological capabilities with humanistic care to achieve harmonious commercial and user value.

Keywords

Precision Marketing, Content Recommendation, User Interest Matching, Algorithmic Personalization