论文标题
使用基于频率的双哈希为推荐系统降低模型尺寸
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
论文作者
论文摘要
具有稀疏输入功能的深神经网络（DNN）已被广泛用于行业的推荐系统。这些模型具有很大的内存要求，需要大量的培训数据。较大的型号通常需要成本数百万美元的成本，以与推理服务进行存储和通信。在本文中，我们提出了一种混合散列方法，以结合频率哈希和双重散列技术，以减少模型尺寸，而不会损害性能。我们在两个产品表面上评估了所提出的模型。在这两种情况下，实验结果都表明，我们可以将模型尺寸降低约90％，同时与原始基线保持相同。
Deep Neural Networks (DNNs) with sparse input features have been widely used in recommender systems in industry. These models have large memory requirements and need a huge amount of training data. The large model size usually entails a cost, in the range of millions of dollars, for storage and communication with the inference services. In this paper, we propose a hybrid hashing method to combine frequency hashing and double hashing techniques for model size reduction, without compromising performance. We evaluate the proposed models on two product surfaces. In both cases, experiment results demonstrated that we can reduce the model size by around 90 % while keeping the performance on par with the original baselines.
