A deep reinforcement learning based long-term recommender system
作者：
Highlights：
• A novel top-N interactive recommender system based on deep reinforcement learning is proposed.
• The interactions between recommender system and users are simulated by recurrent neural networks.
• The proposed model can deal with both cold-start and warm-start scenarios.
• Reinforcement learning and supervised learning are employed to optimize the proposed model for long-term recommendation accuracy.
• Experiments and comparisons are conducted to show the merits of the proposed model.
摘要
•A novel top-N interactive recommender system based on deep reinforcement learning is proposed.•The interactions between recommender system and users are simulated by recurrent neural networks.•The proposed model can deal with both cold-start and warm-start scenarios.•Reinforcement learning and supervised learning are employed to optimize the proposed model for long-term recommendation accuracy.•Experiments and comparisons are conducted to show the merits of the proposed model.
论文关键词：Recommender system,Deep reinforcement learning,Long-term recommendation,Cold-start
论文评审过程：Received 27 May 2020, Revised 16 December 2020, Accepted 17 December 2020, Available online 24 December 2020, Version of Record 24 December 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106706
