Model Size Reduction Using Frequency Based Double Hashing For Recommender Systems
Zhang Caojin, Liu Yicun, Xie Yuanpu, Ktena Sofia Ira, Tejani Alykhan, Gupta Akshay, Myana Pranay Kumar, Dilipkumar Deepak, Paul Suvadip, Ihara Ikuhiro, Upadhyaya Prasang, Huszar Ferenc, Shi Wenzhe. Arxiv 2020
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.
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