16 citations · 32 across the 11 of their papers we have counts for
5 papers · 1 filter
DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
Hangtong Xu, Yuanbo Xu, Yongjian Yang +2
Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechan…
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
Hangtong Xu, Yuanbo Xu, Chaozhuo Li +1
Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems. Conventional approaches often rely on the assumption that user prefer…
Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
Hangtong Xu, Yuanbo Xu, Yongjian Yang
Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items. However, the presence of various unmeasured…
Deep Generative Imputation Model for Missing Not At Random Data
Jialei Chen, Yuanbo Xu, Pengyang Wang +1
Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At…
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
Yiheng Jiang, Yuanbo Xu, Yongjian Yang +3
In this paper, we present a MLP-like architecture for sequential recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token communications. In designing Triangula…