  Industrial search and recommendation systems mostly follow the classic
multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and
re-ranking stages. To account for system efficiency, simple vector-product
based models are commonly deployed in the pre-ranking stage. Recent works
consider distilling the high knowledge of large ranking models to small
pre-ranking models for better effectiveness. However, two major challenges in
pre-ranking system still exist: (i) without explicitly modeling the performance
gain versus computation cost, the predefined latency constraint in the
pre-ranking stage inevitably leads to suboptimal solutions; (ii) transferring
the ranking teacher's knowledge to a pre-ranking student with a predetermined
handcrafted architecture still suffers from the loss of model performance. In
this work, a novel framework AutoFAS is proposed which jointly optimizes the
efficiency and effectiveness of the pre-ranking model: (i) AutoFAS for the
first time simultaneously selects the most valuable features and network
architectures using Neural Architecture Search (NAS) technique; (ii) equipped
with ranking model guided reward during NAS procedure, AutoFAS can select the
best pre-ranking architecture for a given ranking teacher without any
computation overhead. Experimental results in our real world search system show
AutoFAS consistently outperforms the previous state-of-the-art (SOTA)
approaches at a lower computing cost. Notably, our model has been adopted in
the pre-ranking module in the search system of Meituan, bringing significant
improvements.
