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：现有方法通常仅在聚合用户行为序列后自适应调整表示。这种对整个用户序列进行粗粒度重加权的方法，限制了模型准确建模不同场景下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，我们提出了一种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的块顺序堆叠组成。每个模块首先部署一个参数个性化单元，通过重新定义基础特征在粗粒度层面整合场景信息。随后，我们整合经场景自适应调整的表示特征作为上下文信息。通过残差连接，将该上下文融入每个历史行为的表示中，从而在场景层面实现上下文感知的细粒度行为表示定制，进而支持场景感知的用户兴趣建模。