47 citations · 154 across the 20 of their papers we have counts for
20 papers
A Unified Invariant Learning Framework for Graph Classification
Yongduo Sui, Jie Sun, Shuyao Wang +4
Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize sta…
Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects
Dong Yao, Caizhi Tang, Qing Cui +1
Data from observational studies (OSs) is widely available and readily obtainable yet frequently contains confounding biases. On the other hand, data derived from randomized control…
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
Yueqi Wang, Yoni Halpern, Shuo Chang +9
Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user's positive interests, less…
Leveraging Large Language Models for Pre-trained Recommender Systems
Zhixuan Chu, Hongyan Hao, Xin Ouyang +9
Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectivel…
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
Yan Wang, Zhixuan Chu, Xin Ouyang +10
Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behavior…
ALT: An Automatic System for Long Tail Scenario Modeling
Ya-Lin Zhang, Jun Zhou, Yankun Ren +5
In this paper, we consider the problem of long tail scenario modeling with budget limitation, i.e., insufficient human resources for model training stage and limited time and compu…