In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Sequential recommendation aims to recommend the next item that matches a user'sinterest,
based on the sequence of items he/she interacted with before. Scrutinizingprevious studies …

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

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …

Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …

With the greater emphasis on privacy and security in our society, the problem of graph
unlearning—revoking the influence of specific data on the trained GNN model, is drawing …

Conventional recommender systems (RSs) face challenges in precisely capturing users' fine-
grained preferences. Large language models (LLMs) have shown capabilities in …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

Recent years have witnessed the great successes of embedding-based methods in
recommender systems. Despite their decent performance, we argue one potential limitation …

Leading sequential recommendation (SeqRec) models adopt empirical risk minimization
(ERM) as the learning framework, which inherently assumes that the training data (historical …