关键词: 多模态资源画像, 图书馆, AI大模型, 资源优化, 用户体验, 信息行为

Abstract:

[Purpose/Significance] With the rapid advancement of artificial intelligence (AI) technologies, libraries are transforming their service models and content offerings. Large AI models have opened up broader development opportunities for smart libraries. However, the rational adoption and application of these models has posed a significant challenge to libraries. This study employs multimodal resource profiling to conduct research on the optimization of large AI model utilization in libraries, revealing the intrinsic relationships among various types of library resource data. Based on these insights, the optimization methods and related strategies are extracted to enhance the efficiency of library resource utilization and improve user experience. [Method/Process] Multimodal resource profiling is a comprehensive representation that captures the intrinsic characteristics of library resources through tag extraction, aggregation analysis, and visualization of diverse data generated within the libraries. By utilizing a novel clustering algorithm, it overcomes the high sensitivity to input parameters characteristic of traditional algorithms and achieves natural clustering across resources with varying densities, thereby enabling the generation of accurate multimodal resource profiles. The resource profiling model provides a theoretical foundation for optimizing the deployment and utilization of large AI models in libraries, while also delivering rich data support for subsequent AI model applications. The adoption strategy proposed in this study is divided into two aspects: model selection and model utilization. Model selection focuses on compatibility and accuracy to achieve an optimal match between the model and both library resources and user needs. Model utilization emphasizes the effectiveness and usability of the output, thereby enhancing operational efficiency and user experience. Based on this framework, the overall operational mechanism of the adoption optimization strategy is designed around continuous model monitoring, real-time collection of user feedback, iterative model updates, and dynamic adjustment of multimodal resource profiles. [Results/Conclusions] This study takes a public digital library on "Telegram" as a case study to generate multimodal resource profiles, which meticulously categorize user groups, interests, and emotional intensities. By integrating the large AI model adoption optimization strategy with the outcomes of multimodal resource profiling, the model autonomously identifies the most task-relevant features, reducing the need for manual intervention. Not only does it achieve high prediction accuracy, but the explanatory feature weights it outputs also provide a quantifiable basis for service optimization. Through comparative experiments with commonly used structural modules, the proposed method demonstrates significant advantages over traditional recommendation systems in terms of both resource utilization efficiency and user engagement. This study lays the foundation for the future development of library technology and opens up new possibilities for the application of multimodal resource profiling.

Key words: multimodal resource profiling, libraries, AI large models, resource optimization, user experience, information behavior