场景自适应细粒度个性化网络：根据场景上下文定制用户行为表征
Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context
AI总结：
本文提出场景自适应细粒度个性化网络SFPNet，通过场景定制块和残差连接对每个历史行为进行场景级细粒度定制，以提升多场景推荐中的用户兴趣建模能力。
摘要：
现有方法通常仅在聚合用户行为序列之后才自适应地调整表征。这种对整个用户序列进行重新加权的粗粒度方法，阻碍了模型准确建模用户兴趣在不同场景间迁移的能力。为了增强模型在每个场景中从历史行为序列捕捉用户兴趣的能力，我们开发了一个名为场景自适应细粒度个性化网络（SFPNet）的排序框架，该框架设计了一种用于多场景个性化推荐的细粒度方法。具体而言，SFPNet 由一系列顺序堆叠的称为场景定制块（Scenario-Tailoring Block）的模块组成。每个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，我们整合场景自适应调整后的特征表征作为上下文信息。通过采用残差连接，我们将该上下文信息融入每个历史行为的表征中，从而在场景层面实现对行为表征的上下文感知细粒度定制，进而支持场景感知的用户兴趣建模。
英文摘要：
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.
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。
