  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.
