摘 要
随着信息技术的迅猛发展，智能推荐系统已成为提升用户体验和优化信息获取效率的重要工具，而人工智能技术的引入为推荐系统的性能改进提供了全新路径。本研究旨在探讨人工智能在智能推荐系统中的应用潜力及其实际效果，通过融合深度学习、自然语言处理和知识图谱等前沿技术，构建了一种基于多模态数据的个性化推荐框架。研究采用实验对比方法，在大规模真实数据集上验证了所提方法的有效性。结果表明，该框架能够显著提高推荐的准确性和多样性，同时有效缓解冷启动和数据稀疏问题。此外，本研究创新性地引入了用户情境感知机制，使推荐结果更加贴合用户的动态需求。主要贡献在于提出了一种可扩展性强且适应性高的推荐算法，并通过理论分析与实证研究证明其优越性，为未来智能推荐系统的设计与优化提供了重要参考。这一研究成果不仅推动了人工智能技术在推荐领域的深入应用，也为相关行业的智能化转型提供了实践指导。
关键词：智能推荐系统 人工智能 多模态数据
Abstract
With the rapid development of information technology, intelligent recommendation systems have become crucial tools for enhancing user experience and optimizing the efficiency of information acquisition. The integration of artificial intelligence technologies has provided new avenues for improving the performance of recommendation systems. This study investigates the application potential and practical effects of artificial intelligence in intelligent recommendation systems by constructing a personalized recommendation fr amework based on multimodal data through the fusion of cutting-edge technologies such as deep learning, natural language processing, and knowledge graphs. An experimental comparison approach was adopted to validate the proposed method using large-scale real-world datasets. The results demonstrate that this fr amework can significantly improve the accuracy and diversity of recommendations while effectively alleviating issues related to cold start and data sparsity. Additionally, this research innovatively incorporates a user context-aware mechanism, making the recommendation outcomes more aligned with users' dynamic needs. The primary contribution lies in proposing a recommendation algorithm with strong scalability and high adaptability, whose superiority is proven through theoretical analysis and empirical studies, providing significant reference for the design and optimization of future intelligent recommendation systems. This research not only advances the in-depth application of artificial intelligence technologies in the recommendation domain but also offers practical guidance for the intelligent transformation of related industries.
Keyword:Intelligent Recommendation System Artificial Intelligence Multi-Modal Data
目 录
引言 1
1人工智能与推荐系统的基础理论 1
1.1推荐系统的概念与发展历程 1
1.2人工智能技术的核心原理 2
1.3人工智能在推荐系统中的作用机制 2
2数据驱动的智能推荐算法研究 2
2.1基于内容的推荐算法分析 3
2.2协同过滤推荐算法优化 3
2.3深度学习在推荐算法中的应用 3
3人工智能提升推荐系统性能的关键技术 4
3.1用户行为数据的挖掘与建模 4
3.2实时推荐中的算法改进策略 4
3.3冷启动问题的解决方法探讨 5
4智能推荐系统在实际场景中的应用探索 5
4.1电子商务领域的推荐实践 5
4.2在线教育中的个性化推荐研究 6
4.3社交媒体平台的推荐系统设计 6
结论 7
参考文献 8
致谢 9
