Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, its potential is fundamentally constrained by the reliance on purely autoregressive training. This approach focuses solely on predicting the next item while ignoring the rich internal structure of a user's interaction history, thus failing to grasp the underlying intent. To address this limitation, we propose Masked History Learning (MHL), a novel training framework that shifts the objective from simple next-step prediction to deep comprehension of history. MHL augments the standard autoregressive objective with an auxiliary task of reconstructing masked historical items, compelling the model to understand ``why'' an item path is formed from the user's past behaviors, rather than just ``what'' item comes next. We introduce two key contributions to enhance this framework: (1) an entropy-guided masking policy that intelligently targets the most informative historical items for reconstruction, and (2) a curriculum learning scheduler that progressively transitions from history reconstruction to future prediction. Experiments on three public datasets show that our method significantly outperforms state-of-the-art generative models, highlighting that a comprehensive understanding of the past is crucial for accurately predicting a user's future path.

翻译：生成式推荐通过直接生成物品标识符，已成为推荐系统的一种有前景范式。然而，其潜力从根本上受限于对纯自回归训练的依赖。该方式仅专注于预测下一项物品，却忽略了用户交互历史中丰富的内在结构，从而未能捕捉深层意图。为解决此局限，我们提出掩码历史学习（MHL）——一种新型训练框架，将目标从简单的下一步预测转变为对历史的深度理解。MHL 通过辅助任务（重建被掩码的历史物品）增强标准自回归目标，迫使模型理解用户过去行为如何形成物品路径（“为何”形成），而非仅仅预测下一项物品（“是什么”）。我们引入两项关键贡献以强化该框架：（1）基于熵指导的掩码策略，智能定位最具信息量的历史物品进行重建；（2）课程学习调度器，逐步从历史重建过渡至未来预测。在三个公开数据集上的实验表明，我们的方法显著优于先进生成式模型，凸显对过往的全面理解对于准确预测用户未来路径至关重要。