23 citations · 28 across the 3 of their papers we have counts for
8 papers
Reward Shaping for User Satisfaction in a REINFORCE Recommender
Konstantina Christakopoulou, Can Xu, Sai Zhang +10
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key…
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao +3
Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we…
Self-supervised Learning for Large-scale Item Recommendations
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng +8
Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with…
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Jiaqi Ma, Xinyang Yi, Weijing Tang +4
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint par…
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen +4
Recommender system models often represent various sparse features like users, items, and categorical features via embeddings. A standard approach is to map each unique feature valu…
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Xinyang Yi, Zhaoran Wang, Zhuoran Yang +2
We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability . Although there exist numerous algorithms for this pro…