Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL policy from scratch without hurting users' experience is unavoidable due to the requirement of trial-and-error searches. Furthermore, the offline methods, which aim to optimize the policy without online interactions, suffer from the notorious stability problem in value estimation or unbounded variance in counterfactual policy evaluation. To this end, we propose optimizing user retention with Decision Transformer~(DT), which avoids the offline difficulty by translating the RL as an autoregressive problem. However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation. In this work, we, therefore, contribute a series of strategies for tackling the exposed issues. We first articulate an efficient reward prompt by weighted aggregation of meta embeddings for informative reward embedding. Then, we endow a weighted contrastive learning method to solve the discrepancy between training and inference. Furthermore, we design two robust offline metrics to measure user retention. Finally, the significant improvement in the benchmark datasets demonstrates the superiority of the proposed method.

翻译：采用强化学习提升用户留存率，因其对增强用户活跃度具有显著意义而备受关注。然而，受试错搜索机制的限制，在避免损害用户体验的前提下从头训练强化学习策略具有不可避免性。此外，旨在无需在线交互即可优化策略的离线方法，面临价值估计中臭名昭著的稳定性问题或反事实策略评估中方差无界性的困扰。为此，我们提出通过决策Transformer优化用户留存，该方法将强化学习转化为自回归问题，从而规避了离线学习的困难。但将决策Transformer应用于推荐系统存在三大非平凡挑战：(1) 数值奖励值建模能力不足；(2) 策略学习与推荐生成之间的数据差异；(3) 离线性能评估的不可靠性。本研究针对上述问题提出系列解决策略：首先通过元嵌入的加权聚合构建高效奖励提示，实现信息丰富的奖励嵌入；其次引入加权对比学习方法解决训练与推理阶段的数据差异；最后设计两种鲁棒离线指标衡量用户留存率。基准数据集上的显著改进验证了所提方法的优越性。