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推荐系统是解决社会媒体信息过载问题的重要手段。为了解决传统推荐系统无法优化用户长期体验的问题，研究人员提出了交互式推荐系统，并尝试使用深度强化学习优化推荐策略。但是，强化推荐算法面临反馈稀疏、从零学习影响用户体验、物品空间大等问题。为了解决上述问题，提出一种改进的知识增强策略引导的交互式强化推荐模型KGP-DQN。该模型构建行为知识图谱表示模块，将用户历史行为和知识图谱结合，解决反馈稀疏问题；构建策略初始化模块，根据用户历史行为为强化推荐系统提供初始化策略，解决从零学习影响用户体验的问题；构建候选集筛选模块，根据行为知识图谱上的物品表示进行动态聚类，从而减少物品空间，解决动作空间大的问题。在3个真实数据集上进行了实验，实验结果表明，KGP-DQN可以快速有效地对强化推荐系统进行训练，其在3个数据集上的推荐准确率均超过80%。

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