Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

With the recent advances in deep learning, different approaches to improving pre-trained
language models (PLMs) have been proposed. PLMs have advanced state-of-the-art …

Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …

While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Recommender systems rely on user behavior data like ratings and clicks to build
personalization model. However, the collected data is observational rather than …

The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …

Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

Recommendation is a prevalent and critical service in information systems. To provide
personalized suggestions to users, industry players embrace machine learning, more …

Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …