  In large-scale ranking systems, cascading architectures have been widely
adopted to achieve a balance between efficiency and effectiveness. The
pre-ranking module plays a vital role in selecting a subset of candidates for
the subsequent ranking module. It is crucial for the pre-ranking model to
maintain a balance between efficiency and accuracy to adhere to online latency
constraints. In this paper, we propose a novel neural network architecture
called RankTower, which is designed to efficiently capture user-item
interactions while following the user-item decoupling paradigm to ensure online
inference efficiency. The proposed approach employs a hybrid training objective
that learns from samples obtained from the full stage of the cascade ranking
system, optimizing different objectives for varying sample spaces. This
strategy aims to enhance the pre-ranking model's ranking capability and
improvement alignment with the existing cascade ranking system. Experimental
results conducted on public datasets demonstrate that RankTower significantly
outperforms state-of-the-art pre-ranking models.
