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

Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …

Attention is an important mechanism that can be employed for a variety of deep learning
models across many different domains and tasks. This survey provides an overview of the …

Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …

Some recent news recommendation (NR) methods introduce a Pre-trained Language Model
(PLM) to encode news representation by following the vanilla pre-train and fine-tune …

News recommendation is an important technique for personalized news service. Compared
with product and movie recommendations which have been comprehensively studied, the …

In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …

Personalized content-based recommender systems have become indispensable tools for
users to navigate through the vast amount of content available on platforms like daily news …

Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …