Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recently, hyperbolic deep neural networks (HDNNs) have been gaining momentum as the
deep representations in the hyperbolic space provide high fidelity embeddings with few …

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …

Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …

Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking
on an ad or an item, is critical to many online applications such as online advertising and …

Combinatorial features are essential for the success of many commercial models. Manually
crafting these features usually comes with high cost due to the variety, volume and velocity …

Feature engineering has been the key to the success of many prediction models. However,
the process is nontrivial and often requires manual feature engineering or exhaustive …

Many predictive tasks of web applications need to model categorical variables, such as user
IDs and demographics like genders and occupations. To apply standard machine learning …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Factorization Machines (FMs) are a supervised learning approach that enhances the linear
regression model by incorporating the second-order feature interactions. Despite …