  In the realm of search systems, multi-stage cascade architecture is a
prevalent method, typically consisting of sequential modules such as matching,
pre-ranking, and ranking. It is generally acknowledged that the model used in
the pre-ranking stage must strike a balance between efficacy and efficiency.
Thus, the most commonly employed architecture is the representation-focused
vector product based model. However, this architecture lacks effective
interaction between the query and document, resulting in a reduction in the
effectiveness of the search system. To address this issue, we present a novel
pre-ranking framework called RankDFM. Our framework leverages DeepFM as the
backbone and employs a pairwise training paradigm to learn the ranking of
videos under a query. The capability of RankDFM to cross features provides
significant improvement in offline and online A/B testing performance.
Furthermore, we introduce a learnable feature selection scheme to optimize the
model and reduce the time required for online inference, equivalent to a tree
model. Currently, RankDFM has been deployed in the search system of a
shortvideo App, providing daily services to hundreds of millions users.
