Integrating Group-based Preferences from Coarse to Fine for Cold-start Users Recommendation (2025.coling-main)
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| Challenge: | Existing approaches to cross-domain recommendation (CDR) draw on historical purchase records or reviews to generate user representations. |
| Approach: | They propose a model that integrates preferences from coarse to fine levels to improve recommendations for cold-start users. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on three CDR tasks. |
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Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay K. Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
| Challenge: | Current approaches focus on improving ranking performance at the cost of escalating complexity and complicating the task. |
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Few-Shot Learning for Cold-Start Recommendation (2024.lrec-main)
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| Challenge: | Existing methods for cold-start learning and recommendation are brittle to scenarios with few interactions. |
| Approach: | They propose a Few-shot learning method for Cold-Start recommendation that consists of three hierarchical structures that are local and global . |
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Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) have achieved competitive performance with limited labeled data for many NLP tasks. |
| Approach: | They propose a prompt-based data selection method for pre-trained language models fine-tuning under cold-start scenarios. |
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Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning (2023.findings-acl)
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| Challenge: | Existing studies on parameter-efficient fine-tuning methods require additional measures after pre-training and before fine-uning. |
| Approach: | They propose to take parameter-efficient fine-tuning into consideration after pre-training and before fine-uning and use meta-learning to prime a model specifically for parameter-efficiency. |
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Exploiting Rich Textual User-Product Context for Improving Personalized Sentiment Analysis (2023.findings-acl)
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| Challenge: | Typical approaches do not exploit the potential of historical reviews or do not make full use of user/product associations. |
| Approach: | They propose to use historical reviews to initialize user and product representations and incorporate textual associations via a user-product cross-context module. |
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RDRec: Rationale Distillation for LLM-based Recommendation (2024.acl-short)
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| Challenge: | Existing models that bridge users and items through textual prompts for effective semantic reasoning do not consider the underlying rationales behind interactions, such as user preferences and item attributes. |
| Approach: | They propose a rationale distillation recommender model that learns rationales generated by a larger language model (LM) by leveraging reviews related to users and items. |
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From ID to LLM: Rethinking Representation Learning for Recommendation (2026.acl-long)
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| Challenge: | Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations as they capture behavioral and semantic spaces respectively. |
| Approach: | They propose a Profile-then-Embedding framework for recommendation that integrates semantic user and item profiles and a Personalized Embedded stage to encode these profiles into task-aligned recommendation embeddings. |
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Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
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Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration (2025.findings-emnlp)
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| Challenge: | Query-to-Recommendation framework integrates large langucage models into recommendation systems . but it faces training-induced bias and bottlenecks from serialized architecture . |
| Approach: | They propose a parallel recommendation framework that decouples LLMs from candidate pre-selection and direct retrieval over the entire item pool. |
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Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)
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| Challenge: | Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process. |
| Approach: | They propose an ‘extractive’ approach to identify review segments which justify users’ intentions and use it to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets. |
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