基于策略记忆的深度强化学习序列推荐算法研究
Research on Deep Reinforcement Learning Sequential Recommendation Algorithm based on Policy Memory
投稿时间:2021-06-29  修订日期:2021-09-13
DOI:
中文关键词:  序列推荐  策略网络  注意力机制  深度强化学习
英文关键词:Sequential Recommendation  Policy Network  Attention Mechanism  Deep Reinforcement Learning
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目),山东省重点研发计划项目
作者单位邮编
陈卓 青岛科技大学 266061
姜伟豪 青岛科技大学 266061
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中文摘要:
      序列推荐旨在从用户-项目的交互中进行动态的学习和建模,对用户的兴趣变化做出预测,从而提高推荐的准确性,改善用户体验。然而大多数用户-项目的序列并不总是顺序相关的,而是有更灵活的顺序甚至存在噪声。为解决这一问题本文将用户的历史交互存入记忆网络,使用一个策略网络将用户当前的行为模式更细致的划分为短期偏好、长期偏好以及全局偏好,并引入注意力机制生成相应的用户记忆向量,利用深度强化学习算法识别对未来收益较大的项目。在用户和项目的交互中不断更新强化学习网络策略以提高推荐准确性。在两个公共的数据集的实验中表明,本文所提出的模型优于先进的基线。
英文摘要:
      The purpose of sequential recommendation is to learn and model dynamically from the user-project interaction, and predict the change of user interest, so as to improve the accuracy of recommendation and user experience. However, most user project sequences are not always sequential, but have more flexible order and even noise. In order to solve this problem, this paper stores the user's historical interaction into the memory network, uses a strategy network which divides the user's current behavior pattern into short-term preference, long-term preference and global preference. Then we use the attention mechanism to generate the corresponding user-memory vector, and use deep reinforcement learning algorithm to identify projects with greater future benefits. In the interaction between users and projects, reinforcement learning network strategy is constantly updated to improve the accuracy of recommendation. The experiments of two common data sets show that the model proposed in this paper is superior to the advanced baseline.
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