摘 要
互联网信息过载问题日益严重，推荐系统作为关键技术，备受关注。本研究提出一种基于机器学习的混合推荐模型，结合协同过滤、内容分析和深度学习技术，通过注意力机制和用户行为序列建模，提高了推荐精准度和用户体验。使用MovieLens数据集进行实验，结果显示模型在准确率、召回率和F1值上显著优于传统方法，准确率和召回率分别提升15.6%和12.8%。模型还通过时间衰减因子和上下文特征更好地捕捉用户兴趣变化。研究贡献包括新型混合推荐框架、基于注意力机制的特征提取方法和可扩展推荐系统原型。这些成果对提升推荐系统性能和推动个性化服务发展具有重要意义。
关键词：混合推荐模型 机器学习 注意力机制 用户行为序列建模
Abstract
The problem of Internet information overload is becoming increasingly serious, and the recommendation system, as a key technology, has attracted much attention. This study proposes a hybrid recommendation model based on machine learning, combining collaborative filtering, content analysis and deep learning techniques to improve recommendation accuracy and user experience through attention mechanism and user behavior sequence modeling. Experexperiments using the MovieLens dataset, the results show that the model is significantly better than conventional methods in accuracy, recall and F1 value, with accuracy and recall improving by 15.6% and 12.8%, respectively. The model also better captures changes in user interest through time decay factors and context features. Research contributions include the novel hybrid recommendation fr amework, a feature extraction method based on the attention mechanism, and a scalable recommendation system prototype. These achievements are of great significance to improving the performance of the recommendation system and promoting the development of personalized service.
Keywords: Hybrid recommendation model Machine learning Attention mechanism Sequence modeling of user behavior
目 录
1 引言 1
2 推荐系统与机器学习概述 1
2.1 推荐系统的基本原理 1
2.2 机器学习在推荐系统中的应用 1
2.3 基于机器学习的推荐系统优势 2
3 推荐系统中的机器学习算法研究 2
3.1 协同过滤算法及其改进 2
3.2 基于内容的推荐算法分析 3
3.3 混合推荐算法的设计与实现 3
4 推荐系统性能评估与优化 4
4.1 推荐系统评价指标体系构建 4
4.2 机器学习模型的参数调优方法 4
4.3 推荐系统的实时性与可扩展性优化 5
5 基于机器学习的推荐系统实现与应用 5
5.1 系统架构设计与技术选型 5
5.2 数据预处理与特征工程实践 6
5.3 实际应用场景中的效果验证 6
6 结论 7
致 谢 8
参考文献 9
互联网信息过载问题日益严重，推荐系统作为关键技术，备受关注。本研究提出一种基于机器学习的混合推荐模型，结合协同过滤、内容分析和深度学习技术，通过注意力机制和用户行为序列建模，提高了推荐精准度和用户体验。使用MovieLens数据集进行实验，结果显示模型在准确率、召回率和F1值上显著优于传统方法，准确率和召回率分别提升15.6%和12.8%。模型还通过时间衰减因子和上下文特征更好地捕捉用户兴趣变化。研究贡献包括新型混合推荐框架、基于注意力机制的特征提取方法和可扩展推荐系统原型。这些成果对提升推荐系统性能和推动个性化服务发展具有重要意义。
关键词：混合推荐模型 机器学习 注意力机制 用户行为序列建模
Abstract
The problem of Internet information overload is becoming increasingly serious, and the recommendation system, as a key technology, has attracted much attention. This study proposes a hybrid recommendation model based on machine learning, combining collaborative filtering, content analysis and deep learning techniques to improve recommendation accuracy and user experience through attention mechanism and user behavior sequence modeling. Experexperiments using the MovieLens dataset, the results show that the model is significantly better than conventional methods in accuracy, recall and F1 value, with accuracy and recall improving by 15.6% and 12.8%, respectively. The model also better captures changes in user interest through time decay factors and context features. Research contributions include the novel hybrid recommendation fr amework, a feature extraction method based on the attention mechanism, and a scalable recommendation system prototype. These achievements are of great significance to improving the performance of the recommendation system and promoting the development of personalized service.
Keywords: Hybrid recommendation model Machine learning Attention mechanism Sequence modeling of user behavior
目 录
1 引言 1
2 推荐系统与机器学习概述 1
2.1 推荐系统的基本原理 1
2.2 机器学习在推荐系统中的应用 1
2.3 基于机器学习的推荐系统优势 2
3 推荐系统中的机器学习算法研究 2
3.1 协同过滤算法及其改进 2
3.2 基于内容的推荐算法分析 3
3.3 混合推荐算法的设计与实现 3
4 推荐系统性能评估与优化 4
4.1 推荐系统评价指标体系构建 4
4.2 机器学习模型的参数调优方法 4
4.3 推荐系统的实时性与可扩展性优化 5
5 基于机器学习的推荐系统实现与应用 5
5.1 系统架构设计与技术选型 5
5.2 数据预处理与特征工程实践 6
5.3 实际应用场景中的效果验证 6
6 结论 7
致 谢 8
参考文献 9
