Our main contributions are listed as follows:
• Inspired by the success of SENET in the computer vision field, we use the SENET mechanism to learn the weights of features dynamically.
• We introduce three types of Bilinear-Interaction layer to learn feature interactions in a fine-grained way. This is also in contrast to the previous work[6, 9, 10, 19, 20, 23], which calculates the feature interactions with Hadamard product or inner product.
• Combining SENET mechanism with bilinear feature interaction, our shallow model achieves state-of-the-art among the shallow models such as FFM on Criteo and Avazu datasets.
• For further performance gains, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models on Criteo and Avazu datasets.