# Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation

arXiv: https://arxiv.org/abs/2508.05657 · HTML: https://mlsanity.com/paper/2508.05657/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 30 of 36)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Generative Representational Instruction Tuning](https://mlsanity.com/paper/2402.09906/) (2024)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://mlsanity.com/paper/2311.00423/) (2023)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion](https://mlsanity.com/paper/2007.04032/) (2020)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019)
- [CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation](https://mlsanity.com/paper/2310.19488/) (2023)
- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [INSPIRED: Toward Sociable Recommendation Dialog Systems](https://mlsanity.com/paper/2009.14306/) (2020)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [Collaborative Large Language Model for Recommender Systems](https://mlsanity.com/paper/2311.01343/) (2023)
- [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021)
- [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024)
- [Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering](https://mlsanity.com/paper/2009.03376/) (2020)
- [A Large Language Model Enhanced Conversational Recommender System](https://mlsanity.com/paper/2308.06212/) (2023)
- [Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information](https://mlsanity.com/paper/2112.08140/) (2021)
- [Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss](https://mlsanity.com/paper/2310.18700/) (2023)
- [Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation](https://mlsanity.com/paper/2109.07946/) (2021)
- [BARCOR: Towards A Unified Framework for Conversational Recommendation Systems](https://mlsanity.com/paper/2203.14257/) (2022)
- [Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset](https://mlsanity.com/paper/2403.04460/) (2024)
- [Sparks of Artificial General Recommender (AGR): Early Experiments with ChatGPT](https://mlsanity.com/paper/2305.04518/) (2023)
- [Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation](https://mlsanity.com/paper/2310.16738/) (2023)

## Built on by (most cited first, 5 of 5)

- [RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems](https://mlsanity.com/paper/2605.11874/) (2026)
- [ChatShopBuddy: Towards Reliable Conversational Shopping Agents via Reinforcement Learning](https://mlsanity.com/paper/2603.06065/) (2026)
- [The Significance of Style Diversity in Annotation-Free Synthetic Data Generation](https://mlsanity.com/paper/2606.20400/) (2026)
- [Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search](https://mlsanity.com/paper/2609.00618/) (2026)
- [Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders](https://mlsanity.com/paper/2610.17771/) (2026)

## Abstract (arXiv)

Conversational recommender systems (CRSs) enhance recommendation quality by engaging users in multi-turn dialogues, capturing nuanced preferences through natural language interactions. However, these systems often face the false negative issue, where items that a user might like are incorrectly labeled as negative during training, leading to suboptimal recommendations.Expanding the label set through data augmentation presents an intuitive solution but faces the challenge of balancing two key aspects: ensuring semantic relevance and preserving the collaborative information inherent in CRS datasets. To address these issues, we propose a novel data augmentation framework that first leverages an LLM-based semantic retriever to identify diverse and semantically relevant items, which are then filtered by a relevance scorer to remove noisy candidates. Building on this, we introduce a two-stage training strategy balancing semantic relevance and collaborative information. Extensive experiments on two benchmark datasets and user simulators demonstrate significant and consistent performance improvements across various recommenders, highlighting the effectiveness of our approach in advancing CRS performance.