Building a scalable and real-time recommendation system is vital for many businesses
driven by time-sensitive customer feedback, such as short-videos ranking or online ads …

Recommender models are commonly used to suggest relevant items to a user for e-
commerce and online advertisement-based applications. These models use massive …

With the advent of deep learning, neural network-based recommendation models have
emerged as an important tool for tackling personalization and recommendation tasks. These …

Recent years have witnessed an exponential growth of model scale in deep learning-based
recommender systems---from Google's 2016 model with 1 billion parameters to the latest …

Large-scale recommendation systems are characterized by their reliance on high
cardinality, heterogeneous features and the need to handle tens of billions of user actions …

Recommender systems that can learn from cross-session data to dynamically predict the
next item a user will choose are crucial for online platforms. However, existing approaches …

Deep learning-based recommendation has become a widely adopted technique in various
online applications. Typically, a deployed model undergoes frequent re-training to capture …

In this paper, we study the task of cold-start sequential recommendation, where new users
with very short interaction sequences come with time. We cast this problem as a few-shot …

Sequential Recommendation aims to predict the next item by mining out the dynamic
preference from user previous interactions. However, most methods represent each item as …

Deep learning based recommender systems have been extensively explored in recent
years. However, the large number of models proposed each year poses a big challenge for …