Papers by Xubin Ren
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. |
XRec: Large Language Models for Explainable Recommendation (2024.findings-emnlp)
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| Challenge: | Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items. |
| Approach: | They propose a model-agnostic framework that enables large language models to provide comprehensive explanations for user behaviors in recommender systems. |
| Outcome: | The proposed framework outperforms baseline approaches in explainable recommender systems. |
MiniRAG: A Lightweight RAG system with Small Language Models (2026.acl-long)
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| Challenge: | Existing RAG frameworks rely on Large Language Models (LLMs) for all stages of the process, resulting in high computational costs and resource demands. |
| Approach: | They propose a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure and a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities. |
| Outcome: | The proposed system achieves comparable performance to LLM-based methods while requiring only 25% of the storage space. |
EasyRec: Simple yet Effective Language Models for Recommendation (2025.emnlp-main)
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| Challenge: | Existing methods for learning from user-item interaction data rely on unique user and item IDs, which limits their performance in zero-shot learning scenarios. |
| Approach: | They propose an approach that integrates text-based semantic understanding with collaborative signals. |
| Outcome: | The proposed approach outperforms state-of-the-art models in zero-shot recommendation scenarios. |