Learnable embedding vector is one of the most important applications in machine learning,
and is widely used in various database-related domains. However, the high dimensionality …

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 …

Representation learning has been a critical topic in machine learning. In Click-through Rate
Prediction, most features are represented as embedding vectors and learned …

Deep recommender systems (DRS) are critical for current commercial online service
providers, which address the issue of information overload by recommending items that are …

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

Multi-scenario recommender systems (MSRSs) have been increasingly used in real-world
industrial platforms for their excellent advantages in mitigating data sparsity and reducing …

Uplift modeling has shown very promising results in online marketing. However, most
existing works are prone to the robustness challenge in some practical applications. In this …

Deep Recommender Systems (DRS) are increasingly dependent on a large number of
feature fields for more precise recommendations. Effective feature selection methods are …

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

Multi-behavior recommender systems (MBRS) have been commonly deployed on real-world
industrial platforms for their superior advantages in understanding user preferences and …