Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence the increase of computing and storage costs. To save resources while maintaining model performances, we propose SHARK, the model compression practice we have summarized in the recommender system of industrial scenarios. SHARK consists of two main components. First, we use the novel first-order component of Taylor expansion as importance scores to prune the number of embedding tables (feature fields). Second, we introduce a new row-wise quantization method to apply different quantization strategies to each embedding. We conduct extensive experiments on both public and industrial datasets, demonstrating that each component of our proposed SHARK framework outperforms previous approaches. We conduct A/B tests in multiple models on Kuaishou, such as short video, e-commerce, and advertising recommendation models. The results of the online A/B test showed SHARK can effectively reduce the memory footprint of the embedded layer. For the short-video scenarios, the compressed model without any performance drop significantly saves 70% storage and thousands of machines, improves 30\% queries per second (QPS), and has been deployed to serve hundreds of millions of users and process tens of billions of requests every day.

翻译：增大嵌入层尺寸已被证明能有效提升推荐模型性能，但这也导致工业推荐系统中模型规模逐渐超过太字节量级，进而增加了计算与存储成本。为在保持模型性能的同时节约资源，我们提出SHARK——一种在工业场景推荐系统中总结出的模型压缩实践方案。SHARK包含两大核心组件：首先，采用新颖的泰勒展开一阶分量作为重要性评分，对嵌入表（特征域）数量进行剪枝；其次，引入新的行级量化方法，对每个嵌入向量应用差异化量化策略。我们在公开数据集与工业数据集上开展大量实验，证明SHARK框架的每个组件均优于已有方法。我们在快手短视频、电商及广告推荐等多个模型上进行A/B测试，在线实验结果显示SHARK能有效降低嵌入层内存占用。在短视频场景中，压缩模型在性能零损失情况下节省70%存储空间并减少数千台机器，每秒查询数(QPS)提升30%，且已部署服务于数亿用户，每日处理数百亿次请求。