摘要
推荐系统旨在从用户-项目的交互中进行建模,为用户推荐感兴趣的内容,从而提高用户体验.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,提出一种基于策略记忆的深度强化学习序列推荐算法,该算法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地划分为短期偏好、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、强化学习网络的策略以提高推荐准确性.在两个公共数据集的实验中表明,本文所提出的算法与最先进的基线算法相比,召回率指标在2个数据集上分别提升了8.87%和11.20%.
The recommender system aims to build a model from the user-item interaction and recommend the content of interest to users,so as to improve the user experience.However,most user-item sequences are not always sequentially related but have more flexible sequences and even noise.In order to solve this problem,a deep reinforce⁃ment learning sequence recommender algorithm based on strategy memory is proposed.The algorithm stores the user’s historical interaction in the memory network,and then uses a strategy network to divide the user′s current behavior pattern into short-term preference,long-term preference,and global preference,and introduces the attention mecha⁃nism to generate the corresponding user memory vector.The deep reinforcement learning algorithm is used to identify the projects with great benefits in the future.The strategy of the reinforcement learning network is continuously up⁃dated in the interaction between users and items to improve the accuracy of the recommender.Experiments on two public data sets show that the proposed algorithm improves the recall index by 8.87%and 11.20%,respectively,com⁃pared with the most advanced baseline algorithm.
作者
陈卓
姜伟豪
杜军威
CHEN Zhuo;JIANG Weihao;DU Junwei(School of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第8期208-216,共9页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(F030810,61806107)
山东省重点研发计划资助项目(2018GGX101052)。
关键词
推荐系统
强化学习
策略网络
注意力机制
recommender systems
reinforcement learning
policy network
attention mechanism
