  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.
