A straightforward machine learning architecture for user interaction prediction is to treat x as the input features and deploy the off-the-shelf classifiers such as logistic regression.
However, since the original feature vector x is very sparse and high-dimensional, the model will be easily overfitted. Therefore, it is desirable to represent the raw input features in low- dimensional continuous spaces. Moreover, as shown in existing literature, it is crucial to utilize the higher-order combinatorial features to yield good prediction performance [6, 8, 11, 23, 26, 32].  Paraphrase the paragraph: