Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

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

Transformer is a powerful model for text understanding. However, it is inefficient due to its
quadratic complexity to input sequence length. Although there are many methods on …

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 …

Personalized news recommendation is an essential technique for online news services.
News articles usually contain rich textual content, and accurate news modeling is important …

Nowadays, news recommendation has become a popular channel for users to access news
of their interests. How to represent rich textual contents of news and precisely match users' …

Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …

News recommendation is important for online news services. Existing news
recommendation models are usually learned from users' news click behaviors. Usually the …

The most important task in personalized news recommendation is accurate matching
between candidate news and user interest. Most of existing news recommendation methods …

User interest modeling is critical for personalized news recommendation. Existing news
recommendation methods usually learn a single user embedding for each user from their …