标题： Pctx：用于生成推荐的个性化上下文分词 显示英文标题

标题： Pctx: Tokenizing Personalized Context for Generative Recommendation

摘要： 生成式推荐（GR）模型将每个动作标记为几个离散的标记（称为语义ID），并自回归地生成下一个标记作为预测，表现出内存效率、可扩展性和统一检索与排序的潜力。 尽管有这些优势，现有的分词方法是静态且非个性化的。 它们通常仅从物品特征中推导语义ID，假设一个通用的物品相似性，忽略了用户特定的视角。 然而，在自回归范式下，具有相同前缀的语义ID总是获得相似的概率，因此单一的固定映射在所有用户中隐式地强制执行了一个通用的物品相似性标准。 在实际中，同一物品可能根据用户意图和偏好被不同地解释。 为了解决这个问题，我们提出了一种个性化上下文感知的分词器，在生成语义ID时结合用户的历史交互。 这种设计使得同一物品在不同的用户上下文中可以被标记为不同的语义ID，使GR模型能够捕捉多种解释标准并生成更个性化的预测。 在三个公开数据集上的实验表明，NDCG@10相比非个性化的动作标记基线提高了高达11.44%。 我们的代码可在 https://github.com/YoungZ365/Pctx 获取。 显示英文摘要

摘要： Generative recommendation (GR) models tokenize each action into a few discrete tokens (called semantic IDs) and autoregressively generate the next tokens as predictions, showing advantages such as memory efficiency, scalability, and the potential to unify retrieval and ranking. Despite these benefits, existing tokenization methods are static and non-personalized. They typically derive semantic IDs solely from item features, assuming a universal item similarity that overlooks user-specific perspectives. However, under the autoregressive paradigm, semantic IDs with the same prefixes always receive similar probabilities, so a single fixed mapping implicitly enforces a universal item similarity standard across all users. In practice, the same item may be interpreted differently depending on user intentions and preferences. To address this issue, we propose a personalized context-aware tokenizer that incorporates a user's historical interactions when generating semantic IDs. This design allows the same item to be tokenized into different semantic IDs under different user contexts, enabling GR models to capture multiple interpretive standards and produce more personalized predictions. Experiments on three public datasets demonstrate up to 11.44% improvement in NDCG@10 over non-personalized action tokenization baselines. Our code is available at https://github.com/YoungZ365/Pctx.