摘要
随着电子商务的快速发展，用户对个性化服务的需求日益增长，而个性化推荐算法在提升用户体验和平台运营效率方面发挥着关键作用。本研究旨在探索适用于电子商务场景的高效个性化推荐算法，解决传统算法在数据稀疏性和冷启动问题上的不足。研究采用协同过滤、深度学习和知识图谱相结合的方法，构建了一种融合多源信息的混合推荐模型。通过引入用户行为序列特征和商品属性语义关系，该模型能够更精准地捕捉用户偏好并生成高质量推荐结果。实验基于大规模真实电商数据集展开，结果显示所提算法在准确率、召回率和覆盖率等指标上均显著优于现有主流方法。此外，研究还提出一种动态权重调整机制，以适应不同用户群体的行为差异，进一步提升了推荐系统的鲁棒性和泛化能力。本研究的主要贡献在于将深度学习与知识驱动技术相融合，为解决复杂推荐任务提供了新思路，并为电子商务领域的个性化服务优化奠定了理论和技术基础。
关键词：个性化推荐算法；深度学习；知识图谱；协同过滤；动态权重调整机制
Abstract
With the rapid development of e-commerce, the demand for personalized services is growing, and personalized recommendation algorithms play a crucial role in enhancing user experience and platform operational efficiency. This study aims to explore efficient personalized recommendation algorithms tailored for e-commerce scenarios, addressing the limitations of traditional algorithms in data sparsity and cold-start problems. By integrating collaborative filtering, deep learning, and knowledge graphs, a hybrid recommendation model that fuses multi-source information is constructed. The model incorporates user behavior sequence features and semantic relationships of product attributes, enabling more precise capture of user preferences and generation of high-quality recommendations. Experiments were conducted using large-scale real-world e-commerce datasets, and the results demonstrate that the proposed algorithm significantly outperforms existing mainstream methods in terms of accuracy, recall, and coverage. Additionally, a dynamic weight adjustment mechanism is introduced to accommodate behavioral differences across user groups, further enhancing the robustness and generalization capability of the recommendation system. The primary contribution of this research lies in the integration of deep learning with knowledge-driven technologies, offering new insights into solving complex recommendation tasks and establishing a theoretical and technical foundation for optimizing personalized services in the e-commerce domain.
Keywords：Personalized Recommendation Algorithm; Deep Learning; Knowledge Graph; Collaborative Filtering; Dynamic Weight Adjustment Mechanism
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电子商务与个性化推荐的背景 1
(二) 研究意义与价值分析 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与技术路线 2
二、个性化推荐算法基础理论 2
(一) 推荐算法的核心概念 2
(二) 常见推荐算法分类与特点 3
(三) 数据处理与特征提取方法 3
(四) 算法性能评估指标体系 4
三、电子商务场景下的推荐算法应用 4
(一) 用户行为数据的获取与分析 4
(二) 协同过滤算法在电商中的优化 5
(三) 深度学习模型的应用实践 5
(四) 实时推荐系统的实现挑战 6
四、推荐算法改进与创新研究 6
(一) 针对冷启动问题的解决方案 6
(二) 融合多源数据的推荐策略 7
(三) 提升推荐结果多样性的方法 8
(四) 算法效率与用户体验的平衡 8
结 论 10
参考文献 11
