This paper introduces a novel model called Neural Factorization Machine (NFM) for sparse predictive analytics. NFM combines the strengths of Factorization Machines (FMs) and neural networks to effectively model both second-order and higher-order feature interactions in sparse data. FMs are popular for efficiently capturing second-order interactions, but they are limited in their linearity and inability to handle complex, non-linear structures in real-world data. Deep neural networks, while powerful for learning non-linear interactions, are challenging to train due to optimization difficulties and overfitting. NFM introduces a new operation called Bilinear Interaction (Bi-Interaction) pooling, which linearly combines feature embeddings to capture second-order interactions. This operation is then followed by a stack of non-linear layers to learn higher-order interactions. Empirical results on two regression tasks, Frappe and MovieLens, demonstrate that NFM significantly outperforms FM with a 7.3% relative improvement using only one hidden layer. Compared to state-of-the-art deep learning methods like Wide&Deep and DeepCross, NFM achieves better performance with a shallower structure and fewer parameters. The key contributions of this work include: 1. Introducing Bi-Interaction pooling, a novel operation in neural network modeling. 2. Developing NFM to enhance FMs by modeling higher-order and non-linear feature interactions. 3. Extensive experiments on real-world tasks to validate the effectiveness of NFM and its ability to use neural networks for sparse data prediction. NFM's effectiveness is demonstrated through its superior performance, ease of training, and reduced model complexity compared to existing methods.This paper introduces a novel model called Neural Factorization Machine (NFM) for sparse predictive analytics. NFM combines the strengths of Factorization Machines (FMs) and neural networks to effectively model both second-order and higher-order feature interactions in sparse data. FMs are popular for efficiently capturing second-order interactions, but they are limited in their linearity and inability to handle complex, non-linear structures in real-world data. Deep neural networks, while powerful for learning non-linear interactions, are challenging to train due to optimization difficulties and overfitting. NFM introduces a new operation called Bilinear Interaction (Bi-Interaction) pooling, which linearly combines feature embeddings to capture second-order interactions. This operation is then followed by a stack of non-linear layers to learn higher-order interactions. Empirical results on two regression tasks, Frappe and MovieLens, demonstrate that NFM significantly outperforms FM with a 7.3% relative improvement using only one hidden layer. Compared to state-of-the-art deep learning methods like Wide&Deep and DeepCross, NFM achieves better performance with a shallower structure and fewer parameters. The key contributions of this work include: 1. Introducing Bi-Interaction pooling, a novel operation in neural network modeling. 2. Developing NFM to enhance FMs by modeling higher-order and non-linear feature interactions. 3. Extensive experiments on real-world tasks to validate the effectiveness of NFM and its ability to use neural networks for sparse data prediction. NFM's effectiveness is demonstrated through its superior performance, ease of training, and reduced model complexity compared to existing methods.