Search Results for author: Wenchang Ma

Found 7 papers, 7 papers with code

General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout

1 code implementation21 Feb 2024 An Zhang, Wenchang Ma, Pengbo Wei, Leheng Sheng, Xiang Wang

However, we have discovered that this aggregation mechanism comes with a drawback, which amplifies biases present in the interaction graph.

Collaborative Filtering Recommendation Systems +1

Robust Collaborative Filtering to Popularity Distribution Shift

1 code implementation16 Oct 2023 An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-Seng Chua

The popularity shortcut tricks are good for in-distribution (ID) performance but poorly generalized to out-of-distribution (OOD) data, i. e., when popularity distribution of test data shifts w. r. t.

Collaborative Filtering

Discovering Dynamic Causal Space for DAG Structure Learning

1 code implementation5 Jun 2023 Fangfu Liu, Wenchang Ma, An Zhang, Xiang Wang, Yueqi Duan, Tat-Seng Chua

Discovering causal structure from purely observational data (i. e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning.

Causal Discovery Combinatorial Optimization

Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting

1 code implementation6 Mar 2023 An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-Seng Chua

Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges.

Bilevel Optimization Causal Discovery

Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering

1 code implementation20 Oct 2022 An Zhang, Wenchang Ma, Xiang Wang, Tat-Seng Chua

Collaborative filtering (CF) models easily suffer from popularity bias, which makes recommendation deviate from users' actual preferences.

Collaborative Filtering

Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation

2 code implementations EMNLP 2021 Yuning Mao, Wenchang Ma, Deren Lei, Jiawei Han, Xiang Ren

In this paper, we present a systematic analysis that studies whether current seq2seq models, especially pre-trained language models, are good enough for preserving important input concepts and to what extent explicitly guiding generation with the concepts as lexical constraints is beneficial.

Conditional Text Generation Denoising

CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation

1 code implementation EMNLP 2021 Wenchang Ma, Ryuichi Takanobu, Minlie Huang

Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation.

Recommendation Systems Response Generation +1

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