TAMRec: Time-Aware and Monotonic Reinforcement Learning for On-Demand Embedding Dimensions in Streaming Recommendation

In deep streaming recommender systems, fixed embedding dimensions are inefficient for streaming data, where user-item interactions and activity patterns continuously evolve. Existing dynamic adjustment methods, including reinforcement learning-based approaches, suffer from three critical issues: parameter inflation, catastrophic forgetting caused by shrink operations, and unstable rewards for cold-start. To address these challenges, this paper introduces TAMRec, employing a time-aware policy that decouples interaction frequency from capacity demand, and adopts a monotonic Maintain/Enlarge action space to eliminate shrink-induced forgetting. While monotonic expansion risks uncontrolled parameter growth, an adaptive reward mechanism encourages on-demand allocation: enlargement is favored only when it yields prediction performance above a moving baseline. A group-prior fallback further stabilizes learning for cold-start entities with sparse feedback. Theoretically, we prove that the policy converges to a local optimum with near-zero catastrophic forgetting, on-demand optimality, and bounded storage growth under mild assumptions. Experiments on five benchmark datasets demonstrate that TAMRec delivers competitive or better recommendation performance with compact embeddings and less forgetting in streaming settings. We release the datasets and core codes at: https://anonymous.4open.science/r/STARLA-8887YZ/