Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

To alleviate the problem of information explosion, recommender systems are widely
deployed to provide personalized information filtering services. Usually, embedding tables …

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

At the heart of contemporary recommender systems (RSs) are latent factor models that
provide quality recommendation experience to users. These models use embedding …

Latent factor models are the dominant backbones of contemporary recommender systems
(RSs) given their performance advantages, where a unique vector embedding with a fixed …

With the continuous increase of users and items, conventional recommender systems
trained on static datasets can hardly adapt to changing environments. The high-throughput …

Embedding tables are usually huge in click-through rate (CTR) prediction models. To train
and deploy the CTR models efficiently and economically, it is necessary to compress their …

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 …

Latent factor models are the most popular backbones for today's recommender systems
owing to their prominent performance. Latent factor models represent users and items as …

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