Deep Reinforcement Learning based Recommend System using stratified sampling

[摘要] Typically personalized movie recommendation algorithms often adopt a static view of recommend process and only take current rewards into consideration. Thus, they are hard to adapt to the dynamic change of users and items. In this paper, we propose a movie recommend system based on deep reinforcement learning to better accommodate the dynamic property when users' distribution or interest changes. Firstly, we adopt nature DQN algorithm to set up baseline. Second, under the framework of nature DQN, we use Double DQN to solve overestimation and indeed reduce error. Besides, we use stratified sampling rather than random sampling to accelerate convergence. Finally, by testing on Movielens dataset, the experimental results shows that our algorithm is superior to traditional algorithms, and also comparable to the latest algorithms.

[发布机构] Command and Control Engineering College, Army Engineering University, No. 1, HaiFu Road, Guang Hua Road, Qin Huai District, Nanjing City, Jiangsu Province; 210007, China^1

[效力级别] 无线电电子学 [学科分类] 计算机科学（综合）

[关键词] Dynamic changes;Dynamic property;Movie recommendations;Movielens;Random sampling;Stratified sampling