xDeepFM is a novel model that combines explicit and implicit feature interactions for recommender systems. The paper introduces a Compressed Interaction Network (CIN) that explicitly learns high-order feature interactions at the vector level, unlike traditional models that operate at the bit level. The CIN is integrated with a classical deep neural network (DNN) to form the xDeepFM model, which can learn both explicit and implicit feature interactions. The model is evaluated on three real-world datasets and outperforms state-of-the-art models. The CIN is designed to efficiently capture bounded-degree feature interactions and is shown to be effective in modeling explicit high-order feature interactions. The xDeepFM model requires no manual feature engineering and is capable of learning both low- and high-order feature interactions. The paper also discusses the performance of the model on different datasets and the impact of hyperparameters on its performance. The results demonstrate that xDeepFM is effective in learning both explicit and implicit feature interactions, making it a powerful model for recommender systems.xDeepFM is a novel model that combines explicit and implicit feature interactions for recommender systems. The paper introduces a Compressed Interaction Network (CIN) that explicitly learns high-order feature interactions at the vector level, unlike traditional models that operate at the bit level. The CIN is integrated with a classical deep neural network (DNN) to form the xDeepFM model, which can learn both explicit and implicit feature interactions. The model is evaluated on three real-world datasets and outperforms state-of-the-art models. The CIN is designed to efficiently capture bounded-degree feature interactions and is shown to be effective in modeling explicit high-order feature interactions. The xDeepFM model requires no manual feature engineering and is capable of learning both low- and high-order feature interactions. The paper also discusses the performance of the model on different datasets and the impact of hyperparameters on its performance. The results demonstrate that xDeepFM is effective in learning both explicit and implicit feature interactions, making it a powerful model for recommender systems.