Core Insights - The article discusses the development of a new recommendation system framework called TagCF, which aims to enhance user understanding in addition to content understanding, moving from "knowing what" to "understanding why" [2][43]. Group 1: Research Background and Motivation - The research highlights a gap in traditional recommendation systems, which often focus solely on content without understanding user identities and roles [2][5]. - The TagCF framework was developed in collaboration with Kuaishou's algorithm team, the foundational model and application department, and Wuhan University [2][3]. Group 2: Methodology and Framework - TagCF introduces two new tasks: User Role Identification, which models user characteristics and social roles, and Behavioral Logic Modeling, which explores the logical relationships between user roles and item topics [9][10]. - The framework consists of three main modules: a video content understanding platform based on MLLM, a behavioral logic graph exploration platform, and a downstream recommendation system enhancement [16][18][22]. Group 3: Experimental Results - Experiments showed that user role-based modeling statistically outperformed traditional topic modeling, leading to more stable and effective recommendations [7][40]. - The TagCF framework demonstrated significant improvements in recommendation accuracy and diversity, with TagCF-it and TagCF-ut models achieving notable performance metrics [34][36]. Group 4: Challenges and Solutions - The implementation faced challenges such as uncontrolled tag expansion and the need for precise scoring mechanisms [23][24]. - Solutions included constructing a cover set of high-frequency tags to ensure stability and generalizability in industrial applications [25][41]. Group 5: Conclusion and Future Directions - The article concludes that the TagCF framework represents a significant advancement in recommendation systems by integrating user understanding with content understanding, thus bridging the gap between statistical and symbolic modeling [43][45]. - Future work will focus on refining the tag-logic system and exploring its applications across various business scenarios, including e-commerce and search [44][45].