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. |
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| Challenge: | Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items. |
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| Challenge: | Large language models (LLMs) have been gaining in-depth performance in natural language processing domains. |
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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. |
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| Challenge: | Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while simultaneously providing human-intelligible explanations. |
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| Challenge: | Existing approaches to deploy large language models (LLMs) into RecSys have limited prompt length, unstructured item information, and un-constrained generation of recommendations. |
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| Challenge: | Existing approaches to model user-item interactions do not account for high-order interactions. |
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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. |
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Aligning Large Language Models with Recommendation Knowledge (2024.findings-naacl)
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Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Hulikal Keshavan, Lukasz Heldt, Lichan Hong, Ed Chi, Maheswaran Sathiamoorthy
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| Challenge: | Existing text-based recommendation frameworks that use pretrained language models (PLMs) can improve performance on text-related tasks. |
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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
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