摘要
Personalized recommendation is one of the flourishing AI applications in recent years. However, its powerful functionality comes with the need of a significant amount of embeddings, which hinders the model performance by causing large memory footprints. In addition, each inference request requires multiple memory access to these embeddings, which puts even more stress on the already-suffering memory bandwidth. In this paper, we aim to reduce the storage size of the embeddings required by personalized recommendation while inducing minimal impact on the accuracy. To achieve such goals, we take advantage of the fact that not all data contribute equally towards the final result. Small values and less frequently accessed embeddings have little impact on the accuracy. In addition, we opt to avoid any extra steps of training to restore the accuracy since training recommendation models can take hours, consuming a huge amount of computing power, which is undesirable. Instead, to restore accuracy, we further identify the insight that some data, although less frequently accessed, do not offer good storage reduction-accuracy trade-offs. Following these key guidelines, we judiciously choose certain embeddings to apply element-wise pruning, leaving the rest untouched. We are able to prune the embedding data by more than 99% while inducing less than 1% accuracy drop.