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.
翻译:在搜索系统领域,多阶段级联架构是一种流行的方法,通常由匹配、预排序和排序等顺序模块组成。人们普遍认为,预排序阶段使用的模型必须在功效和效率之间取得平衡。因此,最常用的架构是基于表示的向量积模型。然而,这种架构缺乏查询和文档之间的有效交互,从而降低了搜索系统的效果。为了解决这个问题,我们提出了一种名为RankDFM的新型预排序框架。我们的框架以DeepFM作为骨干,并采用一对一训练方法来学习查询下视频的排序。RankDFM跨特征的能力提高了离线和在线A/B测试性能。此外,我们引入了一种可学习的特征选择方案,以优化模型并减少在线推理所需的时间,相当于树模型。目前,RankDFM已经部署在一个短视频应用程序的搜索系统中,为数亿用户提供每天的服务。