基于深度强化学习的智能推荐系统优化研究

随着互联网技术的迅猛发展，信息过载问题日益突出，智能推荐系统作为解决该问题的关键手段，广泛应用于电商、社交平台和内容服务等领域。然而，传统推荐算法在动态环境适应性、用户长期兴趣建模及冷启动问题上存在局限。为提升推荐系统的个性化与实时决策能力，本文提出基于深度强化学习的智能推荐系统优化方法。研究旨在构建一个能够动态响应用户行为变化、有效捕捉长期价值的推荐框架。为此，本文设计了一种融合深度神经网络与多目标强化学习的模型架构，并引入注意力机制以增强对用户历史行为的语义理解。同时，针对奖励稀疏与探索-利用困境问题，提出一种基于课程学习的训练策略，逐步提升模型的学习效率与稳定性。实验基于多个真实场景数据集，涵盖点击率预测、转化率优化及用户留存提升等关键指标，结果表明所提方法在多项评价指标上优于主流推荐模型，尤其在长周期用户价值预测方面表现突出。本研究不仅拓展了深度强化学习在推荐系统中的应用边界，还为构建更智能、自适应的推荐机制提供了新的理论支持与实践路径。

关键词：深度强化学习；智能推荐系统；用户长期兴趣建模

ABSTRACT

With the rapid development of internet technology, the problem of information overload has become increasingly prominent. As a key solution to this issue, intelligent recommendation systems are widely applied in fields such as e-commerce, social platforms, and content services. However, traditional recommendation algorithms face limitations in adapting to dynamic environments, modeling users' long-term interests, and addressing cold-start scenarios. To enhance the personalization and real-time decision-making capabilities of recommendation systems, this paper proposes an optimization method for intelligent recommendation systems based on deep reinforcement learning. The research aims to construct a recommendation fr amework that can dynamically respond to changes in user behavior and effectively capture long-term value. To achieve this goal, we design a model architecture that integrates deep neural networks with multi-ob jective reinforcement learning, incorporating an attention mechanism to improve the semantic understanding of users’ historical behaviors. Additionally, to address the challenges of sparse rewards and the exploration-exploitation dilemma, we introduce a training strategy based on curriculum learning, which gradually improves the model's learning efficiency and stability. Experiments are conducted on multiple real-world datasets, covering key evaluation metrics such as click-through rate prediction, conversion rate optimization, and user retention improvement. The results demonstrate that the proposed method outperforms mainstream recommendation models across various evaluation indicators, particularly excelling in long-term user value prediction. This study not only expands the application boundaries of deep reinforcement learning in recommendation systems but also provides new theoretical support and practical pathways for building more intelligent and adaptive recommendation mechanisms.

KEY WORDS： Deep Reinforcement Learning;Intelligent Recommendation System;User Long-Term Interest Modeling 

目    录

摘    要 I
ABSTRACT II
1  绪论 1
1.1  研究背景和意义 1
1.2  研究现状 1
1.3  研究方法 2
2  深度强化学习在推荐系统中的理论基础 2
2.1  推荐系统的典型模型与演化路径 2
2.2  强化学习的基本原理与核心要素 3
2.3  深度神经网络与强化学习的融合机制 3
2.4  基于DRL的推荐系统框架设计思路 4
2.5  DRL在推荐任务中的优势与挑战 5
3  基于深度强化学习的推荐策略建模与优化 5
3.1  用户行为建模与状态表示方法 5
3.2  动作空间的设计与多目标推荐问题 6
3.3  奖励函数的构建与反馈机制优化 6
3.4  多阶段决策过程中的策略学习算法 7
3.5  推荐多样性与长期用户满意度的平衡 7
4  实验设计与性能评估分析 8
4.1  数据集选择与预处理方法 8
4.2  实验环境配置与参数设置 8
4.3  对比模型与评价指标体系 9
4.4  推荐准确率与覆盖率的实验结果 9
4.5  长期用户价值提升效果分析 10
结论 11
致    谢 12
参考文献 13
 

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