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Chendi Ge

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3 papers

2 author rows

AAAI Conference 2025 Conference Paper

Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation

Chendi Ge
Xin Wang
Ziwei Zhang
Yijian Qin
Hong Chen
Haiyang Wu
Yang Zhang
Yuekui Yang

Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely on manually designed architectures that are often suboptimal and labor-intensive. Additionally, extracting valuable behavioral information from source domains to improve target domain recommendations remains challenging. To address these challenges, we propose Behavior importance-aware Graph Neural Architecture Search (BiGNAS), a framework that jointly optimizes GNN architecture and data importance for CDR. BiGNAS introduces two key components: a Cross-Domain Customized Supernetwork and a Graph-Based Behavior Importance Perceptron. The supernetwork, as a one-shot, retrain-free module, automatically searches the optimal GNN architecture for each domain without the need for retraining. The perceptron uses auxiliary learning to dynamically assess the importance of source domain behaviors, thereby improving target domain recommendations. Extensive experiments on benchmark CDR datasets and a large-scale industry advertising dataset demonstrate that BiGNAS consistently outperforms state-of-the-art baselines. To the best of our knowledge, this is the first work to jointly optimize GNN architecture and behavior data importance for cross-domain recommendation.

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ICML Conference 2025 Conference Paper

Dynamic Mixture of Curriculum LoRA Experts for Continual Multimodal Instruction Tuning

Chendi Ge
Xin Wang 0019
Zeyang Zhang 0001
Hong Chen 0011
Jiapei Fan
Longtao Huang
Hui Xue 0001
Wenwu Zhu 0001

Continual multimodal instruction tuning is crucial for adapting Multimodal Large Language Models (MLLMs) to evolving tasks. However, most existing methods adopt a fixed architecture, struggling with adapting to new tasks due to static model capacity. We propose to evolve the architecture under parameter budgets for dynamic task adaptation, which remains unexplored and imposes two challenges: 1) task architecture conflict, where different tasks require varying layer-wise adaptations, and 2) modality imbalance, where different tasks rely unevenly on modalities, leading to unbalanced updates. To address these challenges, we propose a novel Dynamic Mixture of Curriculum LoRA Experts (D-MoLE) method, which automatically evolves MLLM’s architecture with controlled parameter budgets to continually adapt to new tasks while retaining previously learned knowledge. Specifically, we propose a dynamic layer-wise expert allocator, which automatically allocates LoRA experts across layers to resolve architecture conflicts, and routes instructions layer-wisely to facilitate knowledge sharing among experts. Then, we propose a gradient-based inter-modal continual curriculum, which adjusts the update ratio of each module in MLLM based on the difficulty of each modality within the task to alleviate the modality imbalance problem. Extensive experiments show that D-MoLE significantly outperforms state-of-the-art baselines, achieving a 15 percent average improvement over the best baseline. To the best of our knowledge, this is the first study of continual learning for MLLMs from an architectural perspective.

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ICML Conference 2023 Conference Paper

Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation

Xin Wang 0019
Zirui Pan
Yuwei Zhou
Hong Chen 0011
Chendi Ge
Wenwu Zhu 0001

There exist complex patterns behind the decision-making processes of different individuals across different environments. For instance, in a social recommender system, various user behaviors are driven by highly entangled latent factors from two environments, i. e. , consuming environment where users consume items and social environment where users connect with each other. Uncovering the disentanglement of these latent factors for users can benefit in enhanced explainability and controllability for recommendation. However, in literature there has been no work on social recommendation capable of disentangling user representations across consuming and social environments. To solve this problem, we study co-disentangled representation learning across different environments via proposing the curriculum co-disentangled representation learning (CurCoDis) model to disentangle the hidden factors for users across both consuming and social environments. To co-disentangle joint representations for user-item consumption and user-user social graph simultaneously, we partition the social graph into equal-size sub-graphs with minimum number of edges being cut, and design a curriculum weighing strategy for subgraph training through measuring the complexity of subgraphs via Descartes’ rule of signs. We further develop the prototype-routing optimization mechanism, which achieves co-disentanglement of user representations across consuming and social environments. Extensive experiments for social recommendation demonstrate that our proposed CurCoDis model can significantly outperform state-of-the-art methods on several real-world datasets.

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