Papers by Wensheng Lu
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)
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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao
| Challenge: | Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation. |
| Approach: | They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented . |
| Outcome: | The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines. |
HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking (2026.acl-long)
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| Challenge: | Existing evaluation benchmarks for document chunking are inadequate due to evidence sparsity . evaluators are unable to evaluate different chunking methods due to the evidence sparing . |
| Approach: | They propose a QA benchmark for document chunking and a hierarchical document structuring framework for it. |
| Outcome: | The proposed framework improves document chunking quality within reasonable time consumption. |
Explainable Recommendation with Personalized Review Retrieval and Aspect Learning (2023.acl-long)
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| Challenge: | Recent years have witnessed a growing interest in the development of explainable recommendation models. |
| Approach: | They propose a model that combines prediction and generation tasks to produce more persuasive explanations by obtaining additional information from the training sets. |
| Outcome: | The proposed model outperforms state-of-the-art models on three datasets and shows that it is more persuasive than previous models. |
Aligning Large Language Models for Controllable Recommendations (2024.acl-long)
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| Challenge: | Existing literature focuses on integrating domain-specific knowledge into LLMs to enhance accuracy using a fixed task template. |
| Approach: | They propose a collection of supervised learning tasks augmented with labels derived from a conventional recommender model to improve LLMs’ proficiency in adhering to recommendation-specific instructions. |
| Outcome: | The proposed approach significantly improves the capability of LLMs to respond to instructions within recommender systems, reducing formatting errors while maintaining a high level of accuracy. |