KGQR is the first trial to use knowledge graph prior on interactive recommendation system based on reinforcement learning. "Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning" Getting stated . python >= 3.7 . Download ratings.csv to raw_data folder.

2022年2月13日 · 文章浏览阅读1.4k次。论文介绍了kgqr框架，该框架结合知识图谱(kg)和强化学习(rl)解决交互式推荐系统的样本效率问题。通过利用kg中的先验知识和图神经网络，kgqr能更好地表示用户动态偏好，提高推荐性能。实验表明，kgqr在真实数据集上显著优于其他方法，并降低了所 …

2025年3月24日 · 交互式推荐系统(IRS)以其灵活的推荐策略和考虑最佳的长期用户体验而备受关注，为了处理动态用户偏好，研究人员将强化学习(reinforcement learning。RL)引入到IRS中，RL方法有一个普遍的样本效率问题，即训练有效的推荐策略需要大量的交互数据。这是由于稀疏的用户响应和由大量候选项组成的大的行为 ...

We propose KGQR (Knowledge Graph enhanced Q-learning framework for interactive Recommendation), a novel architecture that extends DQN. Specifically, we integrate graph learning and sequential decision making as a whole to facilitate knowledge in KG and pattern mining in IRS. On one hand, to alleviate data sparsity, the user feedback is modeled ...

2022年5月31日 · 文章浏览阅读1.2k次，点赞2次，收藏8次。本文提出了一种名为kgqr的新型深度强化学习框架，用于解决交互式推荐系统中的样本效率问题。通过利用知识图谱的结构和语义信息，该框架能够更好地处理用户动态偏好和稀疏反馈。通过候选条目的邻居选择和用户状态的图卷积表示，kgqr提高了推荐性能并 ...

KGQR is the first trial to use knowledge graph prior on interactive recommendation system based on reinforcement learning. \n \"Interactive Recommender System via Knowledge\nGraph-enhanced Reinforcement Learning\"\n \n \n Getting stated \n\n. python >= 3.7 \n\n \n \n.

2020年6月18日 · Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal with the dynamic user preference and optimize accumulative utilities, researchers have introduced reinforcement learning (RL) into IRS. However, RL methods share a common issue of sample efficiency, i.e., huge amount ...

2022年11月7日 · 图7所示。kgqr架构的示意图。知识增强的状态表示模块通过循环神经网络和图神经网络保持用户偏好。(我们跳过了kgqr中的候选选择模块和q值网络。) c.基于图强化学习的现实世界应用

4 kgqr methodology 我们提议的框架的概述如图1所示。一般来说，我们的KGQR模型包含四个主要组件:图卷积模块、状态表示模块、候选选择模块和Q-learning网络模块。在交互式推荐过程中，在每个时间步长t, IRS依次向用户推荐项目，并根据用户反馈rt相应地更新后续推荐 ...

based framework KGQR for interactive recommendation to ad-dresses the sparsity issue. By leveraging prior knowledge in KG in both candidate selection and the learning of user preference from sparse user feedback, KGQR can improve sample efficiency of RL-based IRS models. •The dynamic user preference can be represented more precisely