63 citations · 219 across the 20 of their papers we have counts for
8 papers · 1 filter
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
Zhaochun Ren, Na Huang, Yidan Wang +7
Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumul…
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Jie Wang, Fajie Yuan, Mingyue Cheng +6
Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) commu…
Learning Robust Recommenders through Cross-Model Agreement
Yu Wang, Xin Xin, Zaiqiao Meng +3
Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while…
One Person, One Model, One World: Learning Continual User Representation without Forgetting
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou +3
Learning user representations is a vital technique toward effective user modeling and personalized recommender systems. Existing approaches often derive an individual set of model…
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Xin Xin, Xiangnan He, Yongfeng Zhang +2
Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real…