Enterprise AI Analysis
Application Research on Artificial Intelligence-Empowered Library Service Model
By Kaitong Li, Qinqin Yang, Yichao Wang, Jiayi Xie, Zhizhuo Xie, Feifei Peng from Beijing University of Posts and Telecommunications
Abstract: Service model is the general way to deliver services. This study focuses on the library service model with AI empowerment. Through literature analysis, user demand investigations, and system development practices, this paper discusses the innovation and optimization strategy with AI technology for library service model. The traditional library service models are limited, and most of them are query and supply of book resources. With the development of technology and knowledge acquisition, the traditional library service model no longer meets the personalized retrieval needs and cross-domain retrieval needs. In recent years, some libraries have intelligent chat robots, but most of them can only answer some fixed questions instead of answering personalized questions such as books in a certain field. AI technology can significantly improve service efficiency and user experience through natural language processing, intelligent recommendation algorithm and cross-domain resource integration ability. This study designed and implemented an intelligent service system based on WeChat applet and Button Platform, including intelligent search, personalized recommendation, book review aggregation and other functional modules, which improved the library's current service model and solved the problems caused by limited service models.
Executive Impact: Key Findings & Business Value
This analysis distills critical insights from the research, highlighting how AI can revolutionize library operations and user engagement for forward-thinking institutions.
Key Takeaways:
- AI transforms library services beyond traditional models to personalized, efficient systems.
- The study designs and implements an intelligent library service system using AI technologies like NLP and recommendation algorithms.
- User demand for AI integration is high, particularly for intelligent recommendations and consultations.
- The system improves resource utilization and user experience through intelligent search, personalized recommendations, and book review aggregation.
Executive Summary:
This paper presents an application research on an AI-empowered library service model, addressing the limitations of traditional library services which primarily focus on basic book query and supply. Leveraging AI technologies such as natural language processing, intelligent recommendation algorithms, and cross-domain resource integration, the study aims to significantly enhance service efficiency and user experience. It highlights the growing user demand for personalized retrieval and intelligent consultations, moving beyond fixed-question chatbots. The proposed solution involves an intelligent service system built on a WeChat applet and Button Platform, offering features like intelligent search, personalized recommendations, and book review aggregation. This system is designed to modernize library services, making them more adaptive to user needs and improving resource utilization.
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Increased Retrieval Efficiency/Accuracy with AI
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Enhanced Personalized Recommendations with AI
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Users Prioritizing Intelligent Recommendations
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User Willingness to Adopt AI Services
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI in Library Services
The paper focuses on leveraging Artificial Intelligence to transform traditional library service models. It discusses the application of NLP, intelligent recommendation algorithms, and cross-domain resource integration to improve service efficiency and user experience. This includes moving beyond basic book queries to personalized retrieval and interactive intelligent systems.
System Design & Implementation
The study details the design and implementation of an intelligent library service system. This system is based on a WeChat applet and Button Platform, incorporating modules for intelligent search, personalized recommendations, and book review aggregation. It describes the architecture including data source, storage, processing, and interactive display layers, utilizing MySQL, DeepSeek-v3 API for NLP, and LightGCN for recommendations.
User Demand & Impact
A survey of 98 users revealed high demand for AI integration, with 40% prioritizing intelligent recommendations and 28.9% AI consultation. 89.9% expressed willingness to adopt AI services. Empirical results showed AI-enhanced libraries outperformed traditional models with 92.3% satisfaction in retrieval efficiency and 93.2% in personalized recommendations.
78%
of academic library chatbots only handle basic queries, failing complex requests.
AI-Empowered Library Service Workflow
User Input (Text/Voice)
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AI Processes & Analyzes Input
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Extract Effective Book Information
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Crawler Gathers Library Data
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Check Borrowable Status/Count
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Return Relevant Book Information
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| Interaction |
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Impact of AI in Chinese Academic Libraries
A study across 84 libraries in China revealed significant advantages for AI-enhanced models. The observation group, utilizing AI services, achieved 92.30% satisfaction in retrieval efficiency/accuracy, compared to 89.45% in traditional models. For personalized recommendations, AI models reached 93.23% satisfaction, surpassing traditional models at 89.96% (P<0.05). This demonstrates AI's concrete benefits in improving both efficiency and user satisfaction in real-world library contexts.
Calculate Your Library's AI Transformation ROI
Estimate the potential annual savings and reclaimed hours by implementing AI-powered library services in your institution.
Estimated Annual Savings
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Hours Reclaimed Annually
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Our Proven AI Implementation Roadmap
A structured approach to integrate AI into your library services, ensuring seamless transition and maximum impact.
Phase 1: Discovery & Strategy
Conduct in-depth analysis of existing library infrastructure, user needs, and strategic goals. Define AI integration scope and success metrics. Develop a customized AI solution architecture.
Phase 2: Development & Integration
Build intelligent modules (NLP, recommendation engine, search) based on chosen technologies (DeepSeek-v3, LightGCN). Integrate with existing library systems (OPAC, databases) and platforms (WeChat applet).
Phase 3: Testing & Optimization
Perform rigorous testing, including user acceptance testing (UAT) and performance benchmarks. Refine algorithms and user interfaces based on feedback. Implement anti-hallucination mechanisms and data validation pipelines.
Phase 4: Deployment & Training
Roll out the AI-empowered library service model. Provide comprehensive training for library staff and create user guides. Establish continuous monitoring and support for ongoing performance and user satisfaction.
Phase 5: Continuous Improvement
Regularly collect user feedback and analyze usage data. Iterate on AI models and features to adapt to evolving user needs and technological advancements. Ensure system scalability and security.
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