基于深度学习的推荐系统算法研究与应用
摘 要
随着大数据时代的到来，信息过载问题日益突出，推荐系统作为解决这一问题的关键工具，其重要性愈发显著。传统的推荐算法，如基于协同过滤和基于内容的推荐，虽然在一定程度上能够缓解信息过载，但它们在处理复杂、高维的数据时存在局限性。本研究全面概述了深度学习模型及其在推荐系统中的应用基础，深入分析了深度学习推荐系统的核心算法及其在处理复杂数据上的优势。针对特征提取与表示学习、用户行为建模以及冷启动问题，本研究提出了一系列优化策略，包括利用深度学习模型进行高效的特征提取，采用循环神经网络等模型对用户行为进行精准建模，以及设计基于深度学习的混合推荐算法解决冷启动问题。通过电商场景下的应用案例、视频平台的个性化推荐实现以及社交网络的内容分发策略，本研究验证了基于深度学习的推荐系统在实际应用中的有效性和可行性，为推荐系统的未来发展提供了有力支持。
关键词：深度学习 推荐系统 注意力机制
Abstract
With the advent of the era of big data, the problem of information overload is becoming increasingly prominent, and the recommendation system, as a key tool to solve this problem, is becoming more and more important. Traditional recommendation algorithms, such as collaborative-based filtering and content-based recommendation, are able to alleviate information overload to some extent, they have limitations in handling complex, high-dimensional data. This study provides a comprehensive overview of the deep learning model and its application in the recommendation system, and deeply analyzes the core algorithm of the deep learning recommendation system and its advantages in processing complex data. For feature extraction and representation learning, user behavior modeling and cold start problem, this study puts forward a series of optimization strategy, including the use of deep learning model for efficient feature extraction, using circular neural network model for accurate modeling of user behavior, and the design based on deep learning hybrid recommendation algorithm to solve the problem of cold start. Through the application cases in e-commerce scenarios, the personalized recommendation implementation of video platforms and the content distribution strategy of social networks, this study verifies the effectiveness and feasibility of the deep learning in practical application, and provides strong support for the future development of the recommendation system.
Keywords:Deep learning recommendation system attention mechanism
目 录
1 引言 1
2 深度学习推荐系统算法基础 1
2.1 深度学习模型概述 1
2.2 推荐系统核心算法 2
2.3 深度学习在推荐中的应用优势 2
3 基于深度学习的推荐算法优化 3
3.1 特征提取与表示学习 3
3.2 用户行为建模方法 3
3.3 冷启动问题解决方案 4
4 深度学习推荐系统应用实践 4
4.1 电商场景下的应用案例 4
4.2 视频平台个性化推荐实现 5
4.3 社交网络内容分发策略 5
5 结论 6
致 谢 7
参考文献 8
摘 要
随着大数据时代的到来，信息过载问题日益突出，推荐系统作为解决这一问题的关键工具，其重要性愈发显著。传统的推荐算法，如基于协同过滤和基于内容的推荐，虽然在一定程度上能够缓解信息过载，但它们在处理复杂、高维的数据时存在局限性。本研究全面概述了深度学习模型及其在推荐系统中的应用基础，深入分析了深度学习推荐系统的核心算法及其在处理复杂数据上的优势。针对特征提取与表示学习、用户行为建模以及冷启动问题，本研究提出了一系列优化策略，包括利用深度学习模型进行高效的特征提取，采用循环神经网络等模型对用户行为进行精准建模，以及设计基于深度学习的混合推荐算法解决冷启动问题。通过电商场景下的应用案例、视频平台的个性化推荐实现以及社交网络的内容分发策略，本研究验证了基于深度学习的推荐系统在实际应用中的有效性和可行性，为推荐系统的未来发展提供了有力支持。
关键词：深度学习 推荐系统 注意力机制
Abstract
With the advent of the era of big data, the problem of information overload is becoming increasingly prominent, and the recommendation system, as a key tool to solve this problem, is becoming more and more important. Traditional recommendation algorithms, such as collaborative-based filtering and content-based recommendation, are able to alleviate information overload to some extent, they have limitations in handling complex, high-dimensional data. This study provides a comprehensive overview of the deep learning model and its application in the recommendation system, and deeply analyzes the core algorithm of the deep learning recommendation system and its advantages in processing complex data. For feature extraction and representation learning, user behavior modeling and cold start problem, this study puts forward a series of optimization strategy, including the use of deep learning model for efficient feature extraction, using circular neural network model for accurate modeling of user behavior, and the design based on deep learning hybrid recommendation algorithm to solve the problem of cold start. Through the application cases in e-commerce scenarios, the personalized recommendation implementation of video platforms and the content distribution strategy of social networks, this study verifies the effectiveness and feasibility of the deep learning in practical application, and provides strong support for the future development of the recommendation system.
Keywords:Deep learning recommendation system attention mechanism
目 录
1 引言 1
2 深度学习推荐系统算法基础 1
2.1 深度学习模型概述 1
2.2 推荐系统核心算法 2
2.3 深度学习在推荐中的应用优势 2
3 基于深度学习的推荐算法优化 3
3.1 特征提取与表示学习 3
3.2 用户行为建模方法 3
3.3 冷启动问题解决方案 4
4 深度学习推荐系统应用实践 4
4.1 电商场景下的应用案例 4
4.2 视频平台个性化推荐实现 5
4.3 社交网络内容分发策略 5
5 结论 6
致 谢 7
参考文献 8
