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Zilong Li

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4 papers

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AAAI Conference 2026 Conference Paper

Capturing Dynamic User Interests Under Modality Imbalance for Multimodal Sequential Recommendation

Zilong Li
Jia Zhu
Chenglei Huang
Zhangze Chen
Hanghui Guo
Guoqing Ma
Jianxia Ling

Multimodal sequential recommender systems leverage diverse modal inputs to enhance the accuracy and relevance of personalized recommendations. However, existing fusion strategies often struggle to capture intricate cross-modal interactions, especially under the evolving dynamics of user intent. Moreover, they frequently neglect modality imbalance issues, leading to suboptimal utilization of multimodal information. To address these challenges, we propose DuAF-MAT, a novel framework for robust multimodal sequential recommendation. Our approach consists of three key components: (1) a Dual-Aware Adaptive Fusion (DuAF) module dynamically calibrates modality contributions by jointly modeling user preferences and temporal information, enabling the extraction of multimodal features aligned with evolving user interests; (2) by integrating Modality Adversarial Training with the Mixture-of-Experts paradigm, MAT-MoE employs an ensemble of expert generators to dynamically reconstruct missing modality representations, effectively mitigating modality imbalance challenges; (3) to address the inherent sparsity of sequential behavior data, we propose a Multi-Supervised Contrastive Learning strategy that integrates cross-modal alignment and virtual sequence augmentation. This approach enhances user interest modeling by leveraging diverse learning signals, resulting in improved model robustness and generalization capability. Extensive experiments on four public datasets demonstrate that DuAF-MAT significantly outperforms state-of-the-art baselines.

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NeurIPS Conference 2024 Conference Paper

Denoising Diffusion Path: Attribution Noise Reduction with An Auxiliary Diffusion Model

Yiming Lei
Zilong Li
Junping Zhang
Hongming Shan

The explainability of deep neural networks (DNNs) is critical for trust and reliability in AI systems. Path-based attribution methods, such as integrated gradients (IG), aim to explain predictions by accumulating gradients along a path from a baseline to the target image. However, noise accumulated during this process can significantly distort the explanation. While existing methods primarily concentrate on finding alternative paths to circumvent noise, they overlook a critical issue: intermediate-step images frequently diverge from the distribution of training data, further intensifying the impact of noise. This work presents a novel Denoising Diffusion Path (DDPath) to tackle this challenge by harnessing the power of diffusionmodels for denoising. By exploiting the inherent ability of diffusion models to progressively remove noise from an image, DDPath constructs a piece-wise linear path. Each segment of this path ensures that samples drawn from a Gaussian distribution are centered around the target image. This approach facilitates a gradual reduction of noise along the path. We further demonstrate that DDPath adheres to essential axiomatic properties for attribution methods and can be seamlessly integrated with existing methods such as IG. Extensive experimental results demonstrate that DDPath can significantly reduce noise in the attributions—resulting in clearer explanations—and achieves better quantitative results than traditional path-based methods.

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IJCAI Conference 2024 Conference Paper

DFRP: A Dual-Track Feedback Recommendation System for Educational Resources

ChaoJun Meng
Changfan Pan
Zilong Li
Cong Zhou
Xinran Cao
Jia Zhu

The educational disparities among different regions are remarkably significant. The educational resource platform can effectively bridge the educational capability gap between regions. Most of the existing recommendation algorithms only consider interaction history, while we argue that the dependencies between knowledge points and education-related features are crucial for education resource recommendations. To address this, we propose DFRP, an educational resource recommendation platform based on knowledge graphs(KGs) and educational scale feedback. DFRP employs a recommendation algorithm based on teaching pathways and educational dimensions to achieve accurate recommendations and active feedback on educational resources. We also provide a detailed description of the system framework and present a demonstration scenario that uses educational scales for active feedback and KGs to show knowledge point dependencies.

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NeurIPS Conference 2023 Conference Paper

LICO: Explainable Models with Language-Image COnsistency

Yiming Lei
Zilong Li
Yangyang Li
Junping Zhang
Hongming Shan

Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is salience-based visual interpretation, such as Grad-CAM, where the generation of attention maps depends merely on categorical labels. Although existing interpretation methods can provide explainable decision clues, they often yield partial correspondence between image and saliency maps due to the limited discriminative information from one-hot labels. This paper develops a Language-Image COnsistency model for explainable image classification, termed LICO, by correlating learnable linguistic prompts with corresponding visual features in a coarse-to-fine manner. Specifically, we first establish a coarse global manifold structure alignment by minimizing the distance between the distributions of image and language features. We then achieve fine-grained saliency maps by applying optimal transport (OT) theory to assign local feature maps with class-specific prompts. Extensive experimental results on eight benchmark datasets demonstrate that the proposed LICO achieves a significant improvement in generating more explainable attention maps in conjunction with existing interpretation methods such as Grad-CAM. Remarkably, LICO improves the classification performance of existing models without introducing any computational overhead during inference.

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