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arXiv · 2304.02434

Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System

Qihang Zhao·Rui-jie Zhu·Liu Yang·He Yongming·Bo Zhou·Luo Cheng
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Abstract

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.

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Qihang Zhao, Rui-jie Zhu, Liu Yang, He Yongming, Bo Zhou, Luo Cheng. 2023-04-05. Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System. https://arxiv.org/abs/2304.02434

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