刘殊畅-GFN4Ret_Shuchang_2024.pdf

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1、基于生成流网络的用户留存建模by Shuchang,Jul 2024Modeling User Retention through Generative Flow NetworksXiangyu Zhao*City University of Hong KongZiru Liu（刘子儒）City University of Hong KongShuchang Liu Kuaishou TechnologyZijian ZhangCity University of Hong KongQingpeng Cai*Kuaishou TechnologyLantao HuKuaishou Techno

2、logyPeng Jiang*Kuaishou TechnologyHan LiKuaishou TechnologyBin YangKuaishou TechnologyZhenghai XueNanyang Technology University2024 北京站背景和动机传统推荐系统长期价值优化：户与系统的多次交互 Markov Decision Process(MDP)Recommender SystemUserRequestVideo ListImmediateResponseOpen/ReturnAPPLeaveAPPOpen/ReturnAPPrequest tsession

3、isession i+1return time背景和动机传统推荐系统长期价值优化：户与系统的多次交互 Markov Decision Process(MDP)户的交互历史和当前的推荐请求 户状态（i.e.state）推荐列表作为动作 action期价值标 最化未来多步户 reward 的期望（i.e.discounted cumulative reward maximization）解决案：强化学习Recommender SystemUserRequestVideo ListImmediateResponseOpen/ReturnAPPLeaveAPPOpen/ReturnAPPrequest

4、 tsession isession i+1return time背景和动机传统推荐系统长期价值优化：户与系统的多次交互 Markov Decision Process(MDP)户的交互历史和当前的推荐请求 户状态（i.e.state）推荐列表作为动作 action期价值标 最化未来多步户 reward 的期望（i.e.discounted cumulative reward maximization）解决案：强化学习户留存/回访优化：必要性：是户对系统期期望和认同感的有效评估段，与 DAU 直接相关挑战：o Label 延迟性o时 天o 信号稀疏性o每 session 仅个 labelo 不

5、确定性o户在 app 外为法观测Recommender SystemUserRequestVideo ListImmediateResponseOpen/ReturnAPPLeaveAPPOpen/ReturnAPPrequest tsession isession i+1return time留存优化的现有案RLUR 1：actor-critic 框架留存 Critic：预估未来的回访信号 reward1 Cai,Qingpeng,et al.Reinforcing user retention in a billion scale short video recommender syste

6、m.Companion Proceedings of the ACM Web Conference 2023.留存优化的现有案RLUR 1：actor-critic 框架留存 Critic：预估未来的回访信号 reward即时反馈 Critic 预估：假设单次推荐好坏和惊喜性也会积累成 reward，最终对户回访造成影响1 Cai,Qingpeng,et al.Reinforcing user retention in a billion scale short video recommender system.Companion Proceedings of the ACM Web Conf