SAGA：编码多表面用户动作序列的结构注意力生成动作嵌入模型
SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences
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中文总结 AI 辅助
SAGA是一种编码多表面用户动作序列的结构注意力生成动作嵌入模型，通过按字段分词方案实现字段级注意力，生成的用户嵌入可提升下游推荐任务的点击率和转化率。
中文摘要 AI 辅助
现有用于序列推荐的嵌入模型通常在同质动作空间内运行，限制了其捕捉跨不同行为领域的跨表面行为信号的能力。我们提出SAGA，这是一种生成式动作嵌入模型，可将金融服务机构生态系统中涵盖结账、点对点（P2P）交易、应用内交互、电子邮件及账户操作的多表面用户交互序列编码为统一的用户表示，用于下游推荐任务。SAGA的核心是按字段分词方案，将每个动作事件分解为多个字段级标记（如产品、交互、表面），支持字段级注意力及单标记方法无法实现的字段级训练目标。通过对损失公式、分词粒度和训练数据范围的离线 ablation 研究，我们分离出每项设计选择的贡献。与所有 ablation 模型及替代架构相比，集成SAGA生成的用户嵌入的下游模型在不同下游触点上实现了最强的整体点击率和转化率提升。
英文摘要
Prior embedding models for sequential recommendation typically operate within a homogeneous action space, limiting their ability to capture cross-surface behavioral signals spanning distinct behavioral domains. We present SAGA, a generative action embedding model that encodes multi-surface user interaction sequences across a Financial Service organization's ecosystems, from checkout, peer-to-peer (P2P) transactions, in-app engagement, email to account actions, into a unified user representation for downstream recommendation tasks. Central to SAGA is a per-field tokenization schema that decomposes each action event into multiple field-level tokens (e.g. product, interaction, surface), enabling field-level attention and per-field training objectives that fused single-token approaches cannot support. Through an offline ablation study on loss formulation, tokenization granularity and training data scope, we isolate the contribution of each design choice. A downstream model integrated with SAGA-generated user embeddings delivers the strongest overall click and conversion lift across diverse downstream touchpoints, compared to all ablated and alternative architectures.
发表机构
- PayPal AI
机构由 AI 辅助整理，请以论文原文为准。
