标题： 生成推荐的分层优化：连接标记化和生成 显示英文标题

标题： Bi-Level Optimization for Generative Recommendation: Bridging Tokenization and Generation

摘要： 生成推荐作为一种变革性的范式正在兴起，它直接生成推荐项，而不是依赖于匹配。 构建这样的系统通常涉及两个关键组件：(1) 优化分词器以获得合适的项目标识符，和 (2) 基于这些标识符训练推荐器。 现有方法通常将这些组件分开处理——按顺序或交替进行——忽视了它们之间的相互依赖性。 这种分离可能导致不一致：分词器在没有直接来自推荐目标指导的情况下进行训练，可能会产生次优的标识符，从而降低推荐性能。 为了解决这个问题，我们提出了BLOGER，一个用于生成推荐的双层优化框架，它在一个统一的优化过程中显式建模分词器和推荐器之间的相互依赖性。 下层使用分词序列训练推荐器，而上层则根据分词损失和推荐损失优化分词器。 我们采用元学习方法来高效解决这个双层优化问题，并引入梯度手术以减轻上层更新中的梯度冲突，从而确保项目标识符既具有信息量又与推荐对齐。 在真实世界数据集上的大量实验表明，BLOGER始终优于最先进的生成推荐方法，同时保持实际效率，没有显著的额外计算开销，有效地弥合了项目分词和自回归生成之间的差距。 显示英文摘要

摘要： Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically involves two key components: (1) optimizing the tokenizer to derive suitable item identifiers, and (2) training the recommender based on those identifiers. Existing approaches often treat these components separately--either sequentially or in alternation--overlooking their interdependence. This separation can lead to misalignment: the tokenizer is trained without direct guidance from the recommendation objective, potentially yielding suboptimal identifiers that degrade recommendation performance. To address this, we propose BLOGER, a Bi-Level Optimization for GEnerative Recommendation framework, which explicitly models the interdependence between the tokenizer and the recommender in a unified optimization process. The lower level trains the recommender using tokenized sequences, while the upper level optimizes the tokenizer based on both the tokenization loss and recommendation loss. We adopt a meta-learning approach to solve this bi-level optimization efficiently, and introduce gradient surgery to mitigate gradient conflicts in the upper-level updates, thereby ensuring that item identifiers are both informative and recommendation-aligned. Extensive experiments on real-world datasets demonstrate that BLOGER consistently outperforms state-of-the-art generative recommendation methods while maintaining practical efficiency with no significant additional computational overhead, effectively bridging the gap between item tokenization and autoregressive generation.