87 citations · 212 across the 5 of their papers we have counts for
5 papers
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
Minghao Zhao, Le Wu, Yile Liang +7
Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior…
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
Kai Wang, Zhene Zou, Minghao Zhao +7
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-makin…
Intent Disentanglement and Feature Self-supervision for Novel Recommendation
Tieyun Qian, Yile Liang, Qing Li +3
One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail i…
Recommending Accurate and Diverse Items Using Bilateral Branch Network
Yile Liang, Tieyun Qian
Recommender systems have played a vital role in online platforms due to the ability of incorporating users' personal tastes. Beyond accuracy, diversity has been recognized as a key…
Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks
Tieyun Qian, Yile Liang, Qing Li
Matrix completion is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially grap…