Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems
existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering
an end-to-end learning model that emphasizes both low- and high-order feature interactions
DeepFM is a Factorization-Machine (FM) based Neural Network for CTR prediction, to overcome the shortcomings of the state-of-the-art models and to achieve better performance.
DeepFM trains a deep component and an FM component jointly and models low-order feature interactions through FM and models high-order feature interactions through the DNN
DeepFM can be trained end-to-end with a shared input to its “wide” and “deep” parts, with no need of feature engineering besides raw Features.
it does not need any pre-training; 2) it learns both high- and low-order feature interactions; 3) it introduces a sharing strategy of feature Embedding to avoid feature engineering
combines the power of factorization machines for recommendation and deep learning for Feature Learning in a new neural network architecture