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.

通过场景自适应的细粒度方法，本文提出了一种名为 SFPNet 的排序框架，以从历史行为序列中捕捉用户兴趣，并支持场景感知的用户兴趣建模。