Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Online advertising is a marketing approach that uses numerous online channels to target
potential customers for businesses, brands, and organizations. One of the most serious …

Offline model-based optimization aims to find a design that maximizes a property of interest
using only an offline dataset, with applications in robot, protein, and molecule design …

Click-through rate (CTR) prediction model usually consists of three components: embedding
table, feature interaction layer, and classifier. Learning embedding table plays a …

Click-through prediction (CTR) models transform features into latent vectors and enumerate
possible feature interactions to improve performance based on the input feature set …

As a key component in online marketing, uplift modeling aims to accurately capture the
degree to which different treatments motivate different users, such as coupons or discounts …

Feature selection, which is a technique to select key features in recommender systems, has
received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has …

Deep sparse networks are widely investigated as a neural network architecture for
prediction tasks with high-dimensional sparse features, with which feature interaction …

A large-scale industrial recommendation platform typically consists of multiple associated
scenarios, requiring a unified click-through rate (CTR) prediction model to serve them …

Click-through Rate (CTR) prediction is essential for commercial recommender systems.
Recently, to improve the prediction accuracy, plenty of deep learning-based CTR models …