Existing methods often adjust representations adaptively only after aggregating user behavior sequences. This coarse-grained approach to re-weighting the entire user sequence hampers the model's ability to accurately model the user interest migration across different scenarios. To enhance the model's capacity to capture user interests from historical behavior sequences in each scenario, we develop a ranking framework named the Scenario-Adaptive Fine-Grained Personalization Network (SFPNet), which designs a kind of fine-grained method for multi-scenario personalized recommendations. Specifically, SFPNet comprises a series of blocks named as Scenario-Tailoring Block, stacked sequentially. Each block initially deploys a parameter personalization unit to integrate scenario information at a coarse-grained level by redefining fundamental features. Subsequently, we consolidate scenario-adaptively adjusted feature representations to serve as context information. By employing residual connection, we incorporate this context into the representation of each historical behavior, allowing for context-aware fine-grained customization of the behavior representations at the scenario-level, which in turn supports scenario-aware user interest modeling.

翻译：现有方法通常在聚合用户行为序列后仅进行自适应表征调整，这种对整个用户序列进行粗粒度重加权的方式，限制了模型在多场景下准确建模用户兴趣迁移的能力。为增强模型从各场景历史行为序列中捕捉用户兴趣的能力，我们构建了一个名为场景自适应细粒度个性化网络（SFPNet）的排序框架，该框架设计了一种面向多场景个性化推荐的细粒度方法。具体而言，SFPNet由一系列称为场景定制模块（Scenario-Tailoring Block）的组件顺序堆叠而成。每个模块首先通过参数个性化单元，以重新定义基础特征的方式在粗粒度层面融合场景信息；随后，我们将经过场景自适应调整的特征表示整合为上下文信息。通过残差连接机制，将此上下文信息融入每个历史行为的表征中，从而在场景层级实现对行为表示的上下文感知细粒度定制，进而支撑场景感知的用户兴趣建模。