Search Results for author: Qing Cui

Found 12 papers, 1 papers with code

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

no code implementations16 Jan 2024 Zhixuan Chu, Yan Wang, Qing Cui, Longfei Li, Wenqing Chen, Sheng Li, Zhan Qin, Kui Ren

As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests.

Explainable Recommendation Recommendation Systems

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

no code implementations20 Dec 2023 Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, Sheng Li

To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task.

Data-Centric Financial Large Language Models

no code implementations7 Oct 2023 Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou, Yijia Wang, Fei Yu, Hong Chen, Wanqing Xu, Xin Lu, Qing Cui, Longfei Li, Jun Zhou, Sheng Li

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

1 code implementation NeurIPS 2023 Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He

From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.

Data Augmentation Graph Classification +2

Robust Direct Learning for Causal Data Fusion

no code implementations1 Nov 2022 Xinyu Li, Yilin Li, Qing Cui, Longfei Li, Jun Zhou

In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects.

Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

no code implementations13 Dec 2020 Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, Enhong Chen

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.

Click-Through Rate Prediction

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

no code implementations3 Feb 2019 Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.

Recommendation Systems reinforcement-learning +1

KNET: A General Framework for Learning Word Embedding using Morphological Knowledge

no code implementations7 Jul 2014 Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Tie-Yan Liu

In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings.

Information Retrieval Retrieval +2

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