并行潜在推理在序列推荐中的应用

摘要

从稀疏行为序列中捕捉复杂用户偏好始终是序列推荐领域的核心挑战。现有潜在推理方法通过多步推理扩展测试时计算已展现出潜力，但这些方法仅依赖单一轨迹的深度维度扩展，随着推理深度增加会出现收益递减问题。为突破这一局限，我们提出并行潜在推理（PLR）框架，该框架通过同步探索多样化推理轨迹，首次实现了宽度维度的计算扩展。PLR在连续潜在空间中通过可学习的触发令牌构建并行推理流，通过全局推理正则化保持多流多样性，并采用混合推理流聚合机制自适应融合多流输出。在三个真实场景数据集上的大量实验表明，PLR在保持实时推理效率的同时显著超越现有最优基线。理论分析进一步验证了并行推理对提升泛化能力的有效性。本研究为突破现有深度扩展范式、增强序列推荐系统的推理能力开辟了新路径。

English

Capturing complex user preferences from sparse behavioral sequences remains a fundamental challenge in sequential recommendation. Recent latent reasoning methods have shown promise by extending test-time computation through multi-step reasoning, yet they exclusively rely on depth-level scaling along a single trajectory, suffering from diminishing returns as reasoning depth increases. To address this limitation, we propose Parallel Latent Reasoning (PLR), a novel framework that pioneers width-level computational scaling by exploring multiple diverse reasoning trajectories simultaneously. PLR constructs parallel reasoning streams through learnable trigger tokens in continuous latent space, preserves diversity across streams via global reasoning regularization, and adaptively synthesizes multi-stream outputs through mixture-of-reasoning-streams aggregation. Extensive experiments on three real-world datasets demonstrate that PLR substantially outperforms state-of-the-art baselines while maintaining real-time inference efficiency. Theoretical analysis further validates the effectiveness of parallel reasoning in improving generalization capability. Our work opens new avenues for enhancing reasoning capacity in sequential recommendation beyond existing depth scaling.

并行潜在推理在序列推荐中的应用

Parallel Latent Reasoning for Sequential Recommendation

摘要

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