标题： 基于相似性感知的推荐系统嵌入压缩 显示英文标题

标题： Saec: Similarity-Aware Embedding Compression in Recommendation Systems

摘要： 推荐系统依赖于嵌入方法来表示各种特征。实践中面临的一个挑战是，随着特征数量随着时间的推移不断增加，大型嵌入矩阵会导致显著的内存占用。我们提出了一种名为Saec的相似性感知嵌入矩阵压缩方法来解决这一问题。Saec通过将同一领域内的相似特征聚类来减少嵌入矩阵的大小。Saec还采用基于特征频率的快速聚类优化算法，大幅提高聚类速度。我们在腾讯的生产分布式机器学习系统Numerous上实现了Saec，并使用来自QQ手机浏览器的10天特征数据进行了评估。测试结果显示，Saec将嵌入向量的数量减少了两个数量级，压缩了约27倍的嵌入大小，同时保持了相同的AUC和对数损失性能。 显示英文摘要

摘要： Production recommendation systems rely on embedding methods to represent various features. An impeding challenge in practice is that the large embedding matrix incurs substantial memory footprint in serving as the number of features grows over time. We propose a similarity-aware embedding matrix compression method called Saec to address this challenge. Saec clusters similar features within a field to reduce the embedding matrix size. Saec also adopts a fast clustering optimization based on feature frequency to drastically improve clustering time. We implement and evaluate Saec on Numerous, the production distributed machine learning system in Tencent, with 10-day worth of feature data from QQ mobile browser. Testbed experiments show that Saec reduces the number of embedding vectors by two orders of magnitude, compresses the embedding size by ~27x, and delivers the same AUC and log loss performance.