Qinyong Wang

Enhancing Collaborative Filtering with Generative Augmentation

2019

Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply for users with rare interaction data. Most existing hybrid CF methods try to …

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. …

Streaming Ranking Based Recommender Systems

2018

Studying recommender systems under streaming scenarios has become increasingly important because real-world applications produce data continuously and rapidly. However, most existing recommender systems today are designed in the context of an offline setting. Compared with …

Neural Memory Streaming Recommender Networks with Adversarial Training

2018

With the increasing popularity of various social media and E-commerce platforms, large volumes of user behaviour data (e.g., user transaction data, rating and review data) are being continually generated at unprecedented and ever-increasing scales. It …

Social Influence-Based Group Representation Learning for Group Recommendation

2019

As social animals, attending group activities is an indispensable part in people's daily social life, and it is an important task for recommender systems to suggest satisfying activities to a group of users. The major …

LLMRec: Large Language Models with Graph Augmentation for Recommendation

2024

The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as …