Life-long user behavior modeling, i.e., extracting a user's hidden interests
from rich historical behaviors in months or even years, plays a central role in
modern CTR prediction systems. Conventional algorithms mostly follow two
cascading stages: a simple General Search Unit (GSU) for fast and coarse search
over tens of thousands of long-term behaviors and an Exact Search Unit (ESU)
for effective Target Attention (TA) over the small number of finalists from
GSU. Although efficient, existing algorithms mostly suffer from a crucial
limitation: the \textit{inconsistent} target-behavior relevance metrics between
GSU and ESU. As a result, their GSU usually misses highly relevant behaviors
but retrieves ones considered irrelevant by ESU. In such case, the TA in ESU,
no matter how attention is allocated, mostly deviates from the real user
interests and thus degrades the overall CTR prediction accuracy. To address
such inconsistency, we propose \textbf{TWo-stage Interest Network (TWIN)},
where our Consistency-Preserved GSU (CP-GSU) adopts the identical
target-behavior relevance metric as the TA in ESU, making the two stages twins.
Specifically, to break TA's computational bottleneck and extend it from ESU to
GSU, or namely from behavior length 102 to length 104−105, we build a
novel attention mechanism by behavior feature splitting. For the video inherent
features of a behavior, we calculate their linear projection by efficient
pre-computing \& caching strategies. And for the user-item cross features, we
compress each into a one-dimentional bias term in the attention score
calculation to save the computational cost. The consistency between two stages,
together with the effective TA-based relevance metric in CP-GSU, contributes to
significant performance gain in CTR prediction.Comment: Accepted by KDD 202