7 citations · 14 across the 7 of their papers we have counts for
8 papers
Diffusion Model for Interest Refinement in Multi-Interest Recommendation
Yankun Le, Haoran Li, Baoyuan Ou +4
Multi-interest candidate matching plays a pivotal role in personalized recommender systems, as it captures diverse user interests from their historical behaviors. Most existing met…
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…
Learning to Augment for Casual User Recommendation
Jianling Wang, Ya Le, Bo Chang +3
Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large bod…
Recency Dropout for Recurrent Recommender Systems
Bo Chang, Can Xu, Matthieu Lê +5
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories. However, recurrent neural networks (RNNs) are known to have d…
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
Ruohan Zhan, Konstantina Christakopoulou, Ya Le +6
Most existing recommender systems focus primarily on matching users to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content…
Some Theory For Practical Classifier Validation
Eric Bax, Ya Le
We compare and contrast two approaches to validating a trained classifier while using all in-sample data for training. One is simultaneous validation over an organized set of hypot…
Validation of Matching
Ya Le, Eric Bax, Nicola Barbieri +3
We introduce a technique to compute probably approximately correct (PAC) bounds on precision and recall for matching algorithms. The bounds require some verified matches, but those…
Sparse Quadratic Discriminant Analysis and Community Bayes
Ya Le, Trevor Hastie
We develop a class of rules spanning the range between quadratic discriminant analysis and naive Bayes, through a path of sparse graphical models. A group lasso penalty is used to…