Papers with Recommendation
TASER: Table Agents for Schema-guided Extraction and Recommendation (2026.eacl-industry)
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| Challenge: | Real-world financial filings report critical information about an entity’s investment holdings, but they are often buried in messy, multi-page, fragmented tables that are difficult to parse. |
| Approach: | They propose to train a system that converts highly unstructured, multi-page, heterogeneous tables into normalized, schema-conforming outputs. |
| Outcome: | The proposed system outperforms vision-based table detection models by 10.1% and can generate more useful recommendations by 10%. |
LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)
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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li
| Challenge: | Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training. |
| Approach: | They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks. |
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
A Federated Framework for LLM-based Recommendation (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated potential in building generative recommendation systems through fine-tuning user behavior data. |
| Approach: | They propose a federated framework for LLM-based recommendation that combines dynamic parameter aggregation and learning speed for different clients. |
| Outcome: | The proposed framework achieves a more balanced client performance and improved overall performance in a computational and storage-efficient way while safeguarding user privacy well. |
Guided Profile Generation Improves Personalization with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to personalization with LLMs rely on sparse and complex personal contexts, resulting in incomplete interpretation. |
| Approach: | They propose a general method to generate personal profiles in natural language that extracts important, distinctive features from the personal context into concise, descriptive sentences. |
| Outcome: | The proposed method improves personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context. |
Text-like Encoding of Collaborative Information in Large Language Models for Recommendation (2024.acl-long)
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| Challenge: | Existing methods to adapt Large Language Models for Recommendation (LLMRec) do not represent collaborative information in a text-like format, which may not align optimally with LLMs. |
| Approach: | They propose a novel LLMRec method that integrates collaborative information through text-like encoding. |
| Outcome: | Extensive experiments show that BinLLM integrates collaborative information better with LLMs. |