FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction
This addresses the computational efficiency problem for CTR prediction applications, but it is incremental as it builds on an existing model.
The paper tackles the large model size of FiBiNet for CTR prediction by proposing FiBiNet++, which reduces non-embedding parameters by 12x to 16x while improving performance over state-of-the-art methods.
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. 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 application. In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure, which greatly reduces model size while further improves its performance. One of the primary techniques involves our proposed "Low Rank Layer" focused on feature interaction, which serves as a crucial driver of achieving a superior compression ratio for models. Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets. On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods, including FiBiNet.