Graph neural networks (GNNs) are currently one of the most performant and versatile
collaborative filtering methods. Meanwhile, like in traditional collaborative filtering, owing to …

This paper provides a thorough review of recommendation methods from academic
literature, offering a taxonomy that classifies recommender systems (RSs) into categories …

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 …

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

Data sparsity and cold-start problems are persistent challenges in recommendation systems.
Cross-domain recommendation (CDR) is a promising solution that utilizes knowledge from …

Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require
prohibitively large memory during training and inference. Aiming to reduce the memory …

Huge embedding tables in modern Deep Learning Recommender Models (DLRM) require
prohibitively large memory during training and inference. Aiming to reduce the memory …