摘 要
随着人工智能技术的快速发展，智能推荐系统已成为提升用户体验和优化信息匹配效率的重要工具。本研究聚焦于人工智能算法在智能推荐系统中的优化与实践，旨在解决传统推荐算法在数据稀疏性和冷启动问题上的局限性。通过引入深度学习模型和强化学习方法，构建了一种融合用户行为特征与上下文信息的新型推荐框架。该框架能够动态捕捉用户兴趣的变化，并显著提高推荐结果的相关性和多样性。实验采用大规模真实数据集进行验证，结果显示所提出的算法在准确率、召回率及覆盖率等关键指标上均优于现有主流方法。此外，本研究创新性地提出了一种基于图神经网络的交互建模策略，有效缓解了推荐系统中的长尾效应问题。研究表明，人工智能驱动的算法优化不仅提升了推荐系统的性能，还为个性化服务的设计提供了新的思路，具有重要的理论价值和实际应用意义。关键词：智能推荐系统; 深度学习模型; 强化学习方法; 图神经网络; 长尾效应
Abstract
With the rapid development of artificial intelligence technologies, intelligent recommendation systems have become crucial tools for enhancing user experience and optimizing information matching efficiency. This study focuses on the optimization and implementation of artificial intelligence algorithms in intelligent recommendation systems, aiming to address the limitations of traditional recommendation algorithms in handling data sparsity and cold-start problems. By incorporating deep learning models and reinforcement learning approaches, a novel recommendation fr amework is constructed that integrates user behavior characteristics with contextual information. This fr amework can dynamically capture changes in user interests and significantly improve the relevance and diversity of recommendation results. The experimental validation was conducted using large-scale real-world datasets, and the results demonstrate that the proposed algorithm outperforms existing mainstream methods in key metrics such as accuracy, recall, and coverage. Furthermore, this research innovatively proposes an interaction modeling strategy based on graph neural networks, effectively alleviating the long-tail effect issue in recommendation systems. The findings indicate that algorithmic optimization driven by artificial intelligence not only enhances the performance of recommendation systems but also provides new insights into the design of personalized services, holding significant theoretical value and practical application implications.Key words:Intelligent Recommendation System; Deep Learning Model; Reinforcement Learning Method; Graph Neural Network; Long-Tail Effect
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、智能推荐系统的基础理论 2
1.1、推荐系统的概念与分类 2
1.2、人工智能在推荐中的作用 2
1.3、核心算法的初步探讨 2
第2章、算法优化的技术路径 4
2.1、数据处理与特征提取 4
2.2、深度学习模型的应用 4
2.3、优化策略与性能评估 5
第3章、实践中的挑战与解决方案 6
3.1、用户冷启动问题分析 6
3.2、数据稀疏性应对方法 6
3.3、实时推荐技术实现 7
第4章、应用案例与效果分析 8
4.1、典型应用场景设计 8
4.2、推荐效果的量化评估 8
4.3、实践中的改进方向 8
结 论 10
参考文献 11
