通过生成流网络建模用户留存
Modeling User Retention through Generative Flow Networks
AI总结：
本文提出用生成流网络建模用户留存信号，将留存奖励反向传播至会话内每个推荐项目，并与学习排序目标结合优化即时反馈和留存的累积奖励，通过离线实验和在线A/B测试验证有效性。
摘要：
推荐系统旨在满足用户的日常需求。虽然现有大多数研究侧重于最大化用户与系统的互动，但最近有研究指出，用户返回服务的频率同样反映了推荐的质量和稳定性。然而，优化这种用户留存行为并非易事，并带来了若干挑战，包括难以处理的离开-返回用户活动、稀疏且延迟的信号，以及用户留存与其对推荐列表中每个项目的即时反馈之间的不确定关系。在本工作中，我们将留存信号视为对用户会话结束满意度的整体估计，并提出通过概率流来估计该信号。这种基于流的建模技术能够将留存奖励反向传播到用户会话中的每个推荐项目，并且我们表明，该流与传统学习排序目标相结合，最终优化了即时用户反馈和用户留存两者的非折扣累积奖励。我们通过在两个公开数据集上的离线实证研究以及工业平台上的在线A/B测试验证了所提方法的有效性。
英文摘要：
Recommender systems aim to fulfill the user's daily demands. While most existing research focuses on maximizing the user's engagement with the system, it has recently been pointed out that how frequently the users come back for the service also reflects the quality and stability of recommendations. However, optimizing this user retention behavior is non-trivial and poses several challenges including the intractable leave-and-return user activities, the sparse and delayed signal, and the uncertain relations between users' retention and their immediate feedback towards each item in the recommendation list. In this work, we regard the retention signal as an overall estimation of the user's end-of-session satisfaction and propose to estimate this signal through a probabilistic flow. This flow-based modeling technique can back-propagate the retention reward towards each recommended item in the user session, and we show that the flow combined with traditional learning-to-rank objectives eventually optimizes a non-discounted cumulative reward for both immediate user feedback and user retention. We verify the effectiveness of our method through both offline empirical studies on two public datasets and online A/B tests in an industrial platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
