  CTR prediction has been widely used in the real world. Many methods model
feature interaction to improve their performance. However, most methods only
learn a fixed representation for each feature without considering the varying
importance of each feature under different contexts, resulting in inferior
performance. Recently, several methods tried to learn vector-level weights for
feature representations to address the fixed representation issue. However,
they only produce linear transformations to refine the fixed feature
representations, which are still not flexible enough to capture the varying
importance of each feature under different contexts. In this paper, we propose
a novel module named Feature Refinement Network (FRNet), which learns
context-aware feature representations at bit-level for each feature in
different contexts. FRNet consists of two key components: 1) Information
Extraction Unit (IEU), which captures contextual information and cross-feature
relationships to guide context-aware feature refinement; and 2) Complementary
Selection Gate (CSGate), which adaptively integrates the original and
complementary feature representations learned in IEU with bit-level weights.
Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in
many existing methods to boost their performance. Comprehensive experiments are
conducted to verify the effectiveness, efficiency, and compatibility of FRNet.
