From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

• URL: http://arxiv.org/abs/2604.25291v1
• Date: Tue, 28 Apr 2026 06:57:00 GMT
• Title: From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space
• Abstract summary: GloRank is a generative framework that shifts reranking from selecting local indices to generating global identifiers.<n>We show that GloRank consistently outperforms state-of-the-art baselines and achieves superior robustness in cold-start scenarios.
• Score: 50.72071213515985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In modern recommender systems, list-wise reranking serves as a critical phase within the multi-stage pipeline, finalizing the exposed item sequence and directly impacting user satisfaction by modeling complex intra-list item dependencies. Existing methods typically formulate this task as selecting indices from the local input list. However, this approach suffers from a semantically inconsistent action space: the same output neuron (logits) represents different items across different samples, preventing the model from establishing a stable, intrinsic understanding of the items. To address this, we propose GloRank (Global Action Space Ranker), a generative framework that shifts reranking from selecting local indices to generating global identifiers. Specifically, we represent items as sequences of discrete tokens and reformulate reranking as a token generation task. This design effectively decouples the scoring mechanism from the variable input order, ensuring that items are evaluated against a consistent global standard. We further enhance this with a two-stage optimization pipeline: a supervised pre-training phase to initialize the model with high-quality demonstrations, followed by a reinforcement learning-based post-training phase to directly maximize list-wise utility. Extensive experiments on two public benchmarks and a large-scale industrial dataset, coupled with online A/B tests, demonstrate that GloRank consistently outperforms state-of-the-art baselines and achieves superior robustness in cold-start scenarios.

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