User Retention-oriented Recommendation with Decision Transformer

Abstract

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.

arXiv comment9 pages, 5 figures

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• LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
• Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
• RePlay: a Recommendation Framework for Experimentation and Production Use
• Modeling User Retention through Generative Flow Networks
• Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
• A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning