Author name cluster

Fuji Ren

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

AAAI Conference 2026 Conference Paper

MvP-ECR: Multi-Perspective Emotion-Cause Reasoning for Empathetic Dialogue

Yuanyuan He
Guotai Huang
Wei Li
Jiali You
Jiawen Deng
Fuji Ren

The empathetic dialogue systems aim to recognize user emotions and generate appropriate empathetic responses. However, existing approaches predominantly rely on dialogue history, contextual descriptions, and emotion category labels, failing to model the causal relationship between emotions and their underlying triggers. This limitation leads to generated responses that lack grounding, exhibit weak relevance, and suffer from poor interpretability in emotional expression. To address this, we propose MvP-ECR, a multi-perspective emotion cause reasoning framework that explicitly constructs emotion-cause structures to help models focus on the core emotional drivers. Additionally, we introduce an emotion-cause consistency evaluation metric to quantitatively assess a model’s ability to identify causal relationships. Experiments across multiple large language models (LLMs) demonstrate that the MvP-ECR framework can serve as a plug-and-play tool to help the model correctly infer emotions and causes in empathetic conversations, and provide more immersive responses for empathetic responses. All code and data will be publicly released to promote the development of empathy dialogue research.

PDF Details DOI

AAAI Conference 2026 Conference Paper

TMDC: A Two-Stage Modality Denoising and Complementation Framework for Multimodal Sentiment Analysis with Missing and Noisy Modalities

Yan Zhuang
Minhao Liu
Yanru Zhang
Jiawen Deng
Fuji Ren

Multimodal Sentiment Analysis (MSA) aims to infer human sentiment by integrating information from multiple modalities such as text, audio, and video. In real-world scenarios, however, the presence of missing modalities and noisy signals significantly hinders the robustness and accuracy of existing models. While prior works have made progress on these issues, they are typically addressed in isolation, limiting overall effectiveness in practical settings. To jointly mitigate the challenges posed by missing and noisy modalities, we propose a framework called Two-stage Modality Denoising and Complementation (TMDC). TMDC comprises two sequential training stages. In the Intra-Modality Denoising Stage, denoised modality-specific and modality-shared representations are extracted from complete data using dedicated denoising modules, reducing the impact of noise and enhancing representational robustness. In the Inter-Modality Complementation Stage, these representations are leveraged to compensate for missing modalities, thereby enriching the available information and further improving robustness. Extensive evaluations on MOSI, MOSEI, and IEMOCAP demonstrate that TMDC consistently achieves superior performance compared to existing methods, establishing new state-of-the-art results.

PDF Details DOI

EAAI Journal 2025 Journal Article

A Multi-Scale Sparse Channel Transformer Network for image reconstruction of astronomical bright source contamination

Yajuan Zhang
Congcong Shen
Xia Jiang
Bo Qiu
Ali Luo
Fuji Ren
Yuanlu Chen

Bright source contamination has long been a challenging issue in the field of image processing, particularly in applications such as astronomical observations, satellite imaging, and nighttime surveillance. To address this issue, this paper proposes a novel Multi-Scale Sparse Channel Transformer Network (MSCformer) aimed at achieving high-quality image reconstruction under the influence of bright source contamination. The network integrates a Top-k Sparse Attention mechanism with a Channel Attention module, enabling selective focus on the most informative features and adaptive weight allocation across channels. Additionally, a Multi-Scale Dual-Gate Feedforward Network is designed to further enhance the expression of valuable features while suppressing redundant information. Experimental results demonstrate that the proposed method exhibits outstanding performance in practical applications on the Sloan Digital Sky Survey (SDSS) photometric image dataset. Compared to existing state-of-the-art techniques, MSCformer achieves significant performance improvements, with a Peak Signal-to-Noise Ratio (PSNR) of 45. 093 decibel(dB), a Structural Similarity Index Measure(SSIM) of 0. 978, and a Pixel Average Absolute Error (PAAE) of 0. 675. This not only significantly enhances the removal of bright source contamination in the field of astronomy but also provides important reference value for subsequent research in related domains.

Details DOI

AAAI Conference 2025 Conference Paper

Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models

Zheng Hu
Zhe Li
Ziyun Jiao
Satoshi Nakagawa
Jiawen Deng
Shimin Cai
Tao Zhou
Fuji Ren

In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Hyper-Modality Enhancement for Multimodal Sentiment Analysis with Missing Modalities

Yan Zhuang
Minhao Liu
Wei Bai
Yanru Zhang
Wei Li
Jiawen Deng
Fuji Ren

Multimodal Sentiment Analysis (MSA) aims to infer human emotions by integrating complementary signals from diverse modalities. However, in real-world scenarios, missing modalities are common due to data corruption, sensor failure, or privacy concerns, which can significantly degrade model performance. To tackle this challenge, we propose Hyper-Modality Enhancement (HME), a novel framework that avoids explicit modality reconstruction by enriching each observed modality with semantically relevant cues retrieved from other samples. This cross-sample enhancement reduces reliance on fully observed data during training, making the method better suited to scenarios with inherently incomplete inputs. In addition, we introduce an uncertainty-aware fusion mechanism that adaptively balances original and enriched representations to improve robustness. Extensive experiments on three public benchmarks show that HME consistently outperforms state-of-the-art methods under various missing modality conditions, demonstrating its practicality in real-world MSA applications.

PDF Details

JBHI Journal 2016 Journal Article

Examining Accumulated Emotional Traits in Suicide Blogs With an Emotion Topic Model

Fuji Ren
Xin Kang
Changqin Quan

Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limit the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people's daily writings (i. e. , Blogs), and examining these emotional traits that are predictive of suicidal behaviors. A complex emotion topic model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i. e. , accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and nonsuicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors.

Details DOI