FiBiNet++:Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction
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Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed to resolve this problem. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset.However, the large model size of FiBiNet hinders its wider applications.In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure ,which greatly reducess model size while further improves its performance.Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets and has comparable model size with DNN model which is the smallest one among deep CTR models.On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods,including FiBiNet.
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