# C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

arXiv: https://arxiv.org/abs/2201.02732 · HTML: https://mlsanity.com/paper/2201.02732/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 39 references that are in the map.
- First posted on arXiv 4 Jan 2022.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Cross-Modal Contrastive Learning for Text-to-Image Generation](https://mlsanity.com/paper/2101.04702/) (2021)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [An efficient framework for learning sentence representations](https://mlsanity.com/paper/1803.02893/) (2018)
- [CLEAR: Contrastive Learning for Sentence Representation](https://mlsanity.com/paper/2012.15466/) (2020)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion](https://mlsanity.com/paper/2007.04032/) (2020)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019)
- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning](https://mlsanity.com/paper/1804.00079/) (2018)
- [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021)
- [CRSLab: An Open-Source Toolkit for Building Conversational Recommender System](https://mlsanity.com/paper/2101.00939/) (2021)
- [Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users](https://mlsanity.com/paper/2005.12979/) (2020)
- [Learning Neural Templates for Recommender Dialogue System](https://mlsanity.com/paper/2109.12302/) (2021)
- [Deep Conversational Recommender in Travel](https://mlsanity.com/paper/1907.00710/) (2019)
- [Neural Response Generation with Meta-Words](https://mlsanity.com/paper/1906.06050/) (2019)
- [Leveraging Historical Interaction Data for Improving Conversational Recommender System](https://mlsanity.com/paper/2008.08247/) (2020)
- [KECRS: Towards Knowledge-Enriched Conversational Recommendation System](https://mlsanity.com/paper/2105.08261/) (2021)

## Built on by (most cited first, 24 of 24)

- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [Debiased Contrastive Learning of Unsupervised Sentence Representations](https://mlsanity.com/paper/2205.00656/) (2022)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [A Large Language Model Enhanced Conversational Recommender System](https://mlsanity.com/paper/2308.06212/) (2023)
- [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://mlsanity.com/paper/2212.11868/) (2022)
- [TREA: Tree-Structure Reasoning Schema for Conversational Recommendation](https://mlsanity.com/paper/2307.10543/) (2023)
- [U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation](https://mlsanity.com/paper/2305.04774/) (2023)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Parameter-Efficient Conversational Recommender System as a Language Processing Task](https://mlsanity.com/paper/2401.14194/) (2024)
- [Towards Empathetic Conversational Recommender Systems](https://mlsanity.com/paper/2409.10527/) (2024)
- [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022)
- [Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems](https://mlsanity.com/paper/2312.10967/) (2023)
- [Multi-grained Hypergraph Interest Modeling for Conversational Recommendation](https://mlsanity.com/paper/2305.04798/) (2023)
- [Alleviating the Long-Tail Problem in Conversational Recommender Systems](https://mlsanity.com/paper/2307.11650/) (2023)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation](https://mlsanity.com/paper/2208.04104/) (2022)
- [COLA: Improving Conversational Recommender Systems by Collaborative Augmentation](https://mlsanity.com/paper/2212.07767/) (2022)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations](https://mlsanity.com/paper/2411.14459/) (2024)
- [A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems](https://mlsanity.com/paper/2309.07682/) (2023)
- [Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge](https://mlsanity.com/paper/2209.11386/) (2022)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (2025)
- [LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems](https://mlsanity.com/paper/2507.04722/) (2025)
- [Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models](https://mlsanity.com/paper/2511.12579/) (2025)

## Abstract (arXiv)

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.