The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

Sequential recommendation aims to leverage users' historical behaviors to predict their next
interaction. Existing works have not yet addressed two main challenges in sequential …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

The next location recommendation is at the core of various location-based applications.
Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical …

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

Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

In recent years, there are a large number of recommendation algorithms proposed in the
literature, from traditional collaborative filtering to deep learning algorithms. However, the …

Click-Through Rate (CTR) prediction plays an important role in many industrial applications,
such as online advertising and recommender systems. How to capture users' dynamic and …

Traditional industry recommendation systems usually use data in a single domain to train
models and then serve the domain. However, a large-scale commercial platform often …