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

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

Bundle recommendation aims to recommend a bundle of related items to users, which can
satisfy the users' various needs with one-stop convenience. Recent methods usually take …

Recently, graph neural networks (GNN) have been successfully applied to recommender
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …

With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

Retailers such as grocery stores or e-marketplaces often have vast selections of items for
users to choose from. Predicting a user's next purchases has gained attention recently, in …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …

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

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by
understanding their successive historical behaviors. Recently, some methods for SR are …

Recently, micro-video sharing platforms such as Kuaishou and Tiktok have become a major
source of information for people's lives. Thanks to the large traffic volume, short video …