摘 要
随着信息技术的迅猛发展，推荐系统在电子商务、社交网络等领域发挥着日益重要的作用。然而，传统推荐算法面临着数据稀疏性、冷启动等问题，难以满足用户个性化需求。为此，本研究聚焦于机器学习算法在推荐系统中的应用与优化，旨在通过引入先进的机器学习技术提升推荐系统的性能和用户体验。研究基于协同过滤、内容分析等经典推荐方法，融合深度学习、强化学习等新兴技术，构建了多模态融合推荐模型。该模型不仅能够处理大规模稀疏数据，还具备良好的泛化能力。实验结果表明，在多个公开数据集上，所提出的模型相较于传统算法在准确率、召回率等关键指标上有显著提升。特别是针对冷启动问题，通过引入用户行为序列建模和上下文感知机制，有效提高了新用户和新物品的推荐效果。此外，研究提出了一种自适应参数调整策略，使得模型能够在不同应用场景中自动优化超参数配置，增强了系统的灵活性和实用性。本研究为解决推荐系统面临的挑战提供了新的思路和技术手段，对推动智能推荐技术的发展具有重要意义。
关键词：推荐系统 机器学习 多模态融合
Abstract
With the rapid development of information technology, recommendation systems have been playing an increasingly important role in areas such as e-commerce and social networks. However, traditional recommendation algorithms face challenges such as data sparsity and the cold start problem, making it difficult to meet personalized user needs. To address these issues, this study focuses on the application and optimization of machine learning algorithms in recommendation systems, aiming to enhance system performance and user experience through the introduction of advanced machine learning techniques. Based on classical recommendation methods like collaborative filtering and content analysis, this research integrates emerging technologies such as deep learning and reinforcement learning to construct a multimodal fusion recommendation model. This model not only handles large-scale sparse data but also demonstrates excellent generalization capabilities. Experimental results show that, on multiple public datasets, the proposed model significantly outperforms traditional algorithms in key metrics such as accuracy and recall. Particularly for the cold start problem, the effectiveness of recommendations for new users and items is notably improved by incorporating user behavior sequence modeling and context-aware mechanisms. Additionally, an adaptive parameter adjustment strategy is proposed, enabling the model to automatically optimize hyperparameter configurations across different application scenarios, thereby enhancing the flexibility and practicality of the system. This study provides new approaches and technical means to tackle the challenges faced by recommendation systems, contributing significantly to the advancement of intelligent recommendation technologies.
Keyword:Recommendation System Machine Learning Multimodal Fusion
目 录
引言 1
1推荐系统的机器学习基础 1
1.1推荐系统的基本原理 1
1.2传统推荐算法的局限性 2
1.3机器学习算法的优势 2
2基于内容的推荐算法优化 3
2.1内容特征提取方法 3
2.2特征选择与降维技术 3
3协同过滤算法的应用改进 4
3.1用户 4
3.2邻域模型优化策略 5
3.3矩阵分解技术应用 5
4深度学习在推荐中的创新 6
4.1深度神经网络架构设计 6
4.2序列推荐模型探索 6
4.3强化学习的引入与实践 7
结论 7
参考文献 9
致谢 9
