RCLRec：用于生成推荐中稀疏转换建模的反向课程学习
RCLRec: Reverse Curriculum Learning for Modeling Sparse Conversions in Generative Recommendation
AI总结：
RCLRec通过反向课程学习方法，针对生成推荐中稀疏转换问题，提供额外的实例特定监督，提升模型对用户关键决策过程的聚焦能力，实验显示其在广告收入和订单量上表现优异。
摘要：
在大规模推荐系统中，转换目标稀疏，难以优化。生成推荐（GR）通过将多类型行为组织为统一的token序列来缓解数据稀疏性，但转换信号仍未充分建模。尽管近期行为感知GR模型编码行为类型并使用行为感知注意力，但其仍依赖标准注意力处理完整历史，无法提供额外监督。为解决这些问题，我们提出了RCLRec，一种基于反向课程学习的GR框架，用于稀疏转换监督。对于每个转换目标，RCLRec通过在历史中反向选择转换相关项的子序列构建短课程。其语义token与目标转换token一起输入解码器，以联合生成目标。此设计提供额外的实例特定中间监督，缓解转换稀疏性并聚焦用户关键决策过程。我们进一步引入课程质量感知损失，确保所选课程对转换预测具有信息性。在离线数据集和在线A/B测试中，实验表明RCLRec在广告收入和订单量上均取得显著提升。
英文摘要：
Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organizing multi-type behaviors into a unified token sequence with shared representations, but conversion signals remain insufficiently modeled. While recent behavior-aware GR models encode behavior types and employ behavior-aware attention to highlight decision-related intermediate behaviors, they still rely on standard attention over the full history and provide no additional supervision for conversions, leaving conversion sparsity largely unresolved. To address these challenges, we propose RCLRec, a reverse curriculum learning-based GR framework for sparse conversion supervision. For each conversion target, RCLRec constructs a short curriculum by selecting a subsequence of conversion-related items from the history in reverse. Their semantic tokens are fed to the decoder as a prefix, together with the target conversion tokens, under a joint generation objective. This design provides additional instance-specific intermediate supervision, alleviating conversion sparsity and focusing the model on the user's critical decision process. We further introduce a curriculum quality-aware loss to ensure that the selected curricula are informative for conversion prediction. Experiments on offline datasets and an online A/B test show that RCLRec achieves superior performance, with +2.09% advertising revenue and +1.86% orders in online deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
