  Scoring a large number of candidates precisely in several milliseconds is
vital for industrial pre-ranking systems. Existing pre-ranking systems
primarily adopt the \textbf{two-tower} model since the ``user-item decoupling
architecture'' paradigm is able to balance the \textit{efficiency} and
\textit{effectiveness}. However, the cost of high efficiency is the neglect of
the potential information interaction between user and item towers, hindering
the prediction accuracy critically. In this paper, we show it is possible to
design a two-tower model that emphasizes both information interactions and
inference efficiency. The proposed model, IntTower (short for
\textit{Interaction enhanced Two-Tower}), consists of Light-SE, FE-Block and
CIR modules. Specifically, lightweight Light-SE module is used to identify the
importance of different features and obtain refined feature representations in
each tower. FE-Block module performs fine-grained and early feature
interactions to capture the interactive signals between user and item towers
explicitly and CIR module leverages a contrastive interaction regularization to
further enhance the interactions implicitly. Experimental results on three
public datasets show that IntTower outperforms the SOTA pre-ranking models
significantly and even achieves comparable performance in comparison with the
ranking models. Moreover, we further verify the effectiveness of IntTower on a
large-scale advertisement pre-ranking system. The code of IntTower is publicly
available\footnote{https://github.com/archersama/IntTower}
