Papers by Lianghao Xia
RecLM: Recommendation Instruction Tuning (2025.acl-long)
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| Challenge: | Modern recommender systems aim to understand user-item relationships through past interactions, but their effectiveness is limited when handling sparse data or zero-shot scenarios. |
| Approach: | They propose a model-agnostic recommendation instruction-tuning paradigm that integrates large language models with collaborative filtering. |
| Outcome: | The proposed model-agnostic recommendation instruction-tuning paradigm improves performance across various settings and plug-and-play compatibility with state-of-the-art recommender systems. |
RecGPT: A Foundation Model for Sequential Recommendation (2025.emnlp-main)
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| Challenge: | Existing approaches fail in cold-start and cross-domain scenarios where new users or items lack sufficient interaction history. |
| Approach: | They propose a foundation model for sequential recommendation that achieves genuine zero-shot generalization capabilities by deriving item representations exclusively from textual features. |
| Outcome: | The proposed model achieves zero-shot generalization capabilities in cold-start and cross-domain scenarios. |
OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)
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| Challenge: | Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification. |
| Approach: | They propose a graph foundation model that generalizes to unseen graph data with different properties. |
| Outcome: | The proposed model achieves remarkable zero-shot graph learning performance across various settings. |
GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)
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| Challenge: | Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies. |
| Approach: | They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks. |
| Outcome: | Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks. |
LightRAG: Simple and Fast Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing RAG systems rely on flat data representations and inadequate contextual awareness . lightRAG framework incorporates graph structures into text indexing and retrieval processes . |
| Approach: | LightRAG is a framework that integrates graph structures into text indexing and retrieval processes. |
| Outcome: | The proposed framework incorporates graph structures into text indexing and retrieval processes. |
AnyGraph: Graph Foundation Model in the Wild (2026.findings-acl)
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| Challenge: | Existing graph learning models struggle to extract generalizable insights from heterogeneous graph data, requiring extensive fine-tuning and limiting versatility across domains. |
| Approach: | They propose a graph foundation model that can handle key challenges such as Structure Heterogenity and Feature Heterogenicity. |
| Outcome: | The proposed model can handle key challenges such as structure heterogeneity, Feature heterogenity and fast adaptation across domains. |