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.

本论文提出了一种混合哈希方法，将频率哈希和双哈希技术相结合用于模型大小降低，在两个产品表面上的实验结果表明，我们可以将模型大小降低约 90％，同时保持基线模型的性能。

基于频率双哈希的推荐系统模型大小缩减

Model Size Reduction Using Frequency Based Double Hashing for  Recommender Systems

This paper presents a convolutional layer that is able to process sparse
input features. As an example, for image recognition problems this allows an
efficient filtering of signals that do not lie on a dense grid (like pixel
position), but of more general features (such as color values). The presented
algorithm makes use of the permutohedral lattice data structure. The
permutohedral lattice was introduced to efficiently implement a bilateral
filter, a commonly used image processing operation. Its use allows for a
generalization of the convolution type found in current (spatial) convolutional
network architectures.

本文提出了一种卷积层，能够处理稀疏的输入特征。它利用 Permiutohedral 网格数据结构实现了一个波透镜卷积算法，从而使得卷积神经网络具有更广泛的应用场景。