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

Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …

Highly skewed long-tail item distribution is very common in recommendation systems. It
significantly hurts model performance on tail items. To improve tail-item recommendation …

Embedding representations power machine intelligence in many applications, including
recommendation systems, but they are space intensive-potentially occupying hundreds of …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …

The embedding-based representation learning is commonly used in deep learning
recommendation models to map the raw sparse features to dense vectors. The traditional …

Deep recommender systems have achieved promising performance on real-world
recommendation tasks. They typically represent users and items in a low-dimensional …

Practical large-scale recommender systems usually contain thousands of feature fields from
users, items, contextual information, and their interactions. Most of them empirically allocate …

Embedding models have been an effective learning paradigm for high-dimensional data.
However, one open issue of embedding models is that their representations (latent factors) …

Designing an effective loss function plays a crucial role in training deep recommender
systems. Most existing works often leverage a predefined and fixed loss function that could …