Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems

作者：Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszár, Wenzhe Shi · 年份：2020 · DOI：10.1145/3383313.3412227 · 被引用次数：43 · 研究领域：Advanced Image and Video Retrieval Techniques、Multimodal Machine Learning Applications、Video Analysis and Summarization

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.