# CRSLab: An Open-Source Toolkit for Building Conversational Recommender System

arXiv: https://arxiv.org/abs/2101.00939 · HTML: https://mlsanity.com/paper/2101.00939/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [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)
- [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020)
- [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)
- [INSPIRED: Toward Sociable Recommendation Dialog Systems](https://mlsanity.com/paper/2009.14306/) (2020)
- [Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue](https://mlsanity.com/paper/1909.03922/) (2019)
- [ConvLab: Multi-Domain End-to-End Dialog System Platform](https://mlsanity.com/paper/1904.08637/) (2019)
- [User Memory Reasoning for Conversational Recommendation](https://mlsanity.com/paper/2006.00184/) (2020)
- [Deep Conversational Recommender in Travel](https://mlsanity.com/paper/1907.00710/) (2019)
- [Leveraging Historical Interaction Data for Improving Conversational Recommender System](https://mlsanity.com/paper/2008.08247/) (2020)

## Built on by (most cited first, 30 of 34)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2305.13112/) (2023)
- [MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers](https://mlsanity.com/paper/2109.00799/) (2021)
- [Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation](https://mlsanity.com/paper/2112.07924/) (2021)
- [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)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant](https://mlsanity.com/paper/2501.17546/) (2025)
- [C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System](https://mlsanity.com/paper/2201.02732/) (2022)
- [Learning Neural Templates for Recommender Dialogue System](https://mlsanity.com/paper/2109.12302/) (2021)
- [LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs](https://mlsanity.com/paper/2204.09220/) (2022)
- [RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models](https://mlsanity.com/paper/2110.07477/) (2021)
- [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 Bias Analysis and Language-Model-Enhanced Data Augmentation](https://mlsanity.com/paper/2310.16738/) (2023)
- [Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2404.11773/) (2024)
- [Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation](https://mlsanity.com/paper/2301.11489/) (2023)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Graph Retrieval-Augmented LLM for Conversational Recommendation Systems](https://mlsanity.com/paper/2503.06430/) (2025)
- [Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset](https://mlsanity.com/paper/2303.06791/) (2023)
- [Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems](https://mlsanity.com/paper/2312.10967/) (2023)
- [Alleviating the Long-Tail Problem in Conversational Recommender Systems](https://mlsanity.com/paper/2307.11650/) (2023)
- [Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User](https://mlsanity.com/paper/2504.20458/) (2025)
- [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)
- [τ-Rec: A Verifiable Benchmark for Agentic Recommender Systems](https://mlsanity.com/paper/2606.10156/) (2026)
- [ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systems](https://mlsanity.com/paper/2509.21371/) (2025)
- [FORCE: A Framework of Rule-Based Conversational Recommender System](https://mlsanity.com/paper/2203.10001/) (2022)
- [RecWizard: A Toolkit for Conversational Recommendation with Modular, Portable Models and Interactive User Interface](https://mlsanity.com/paper/2402.15591/) (2024)

## Abstract (arXiv)

In recent years, conversational recommender system (CRS) has received much attention in the research community. However, existing studies on CRS vary in scenarios, goals and techniques, lacking unified, standardized implementation or comparison. To tackle this challenge, we propose an open-source CRS toolkit CRSLab, which provides a unified and extensible framework with highly-decoupled modules to develop CRSs. Based on this framework, we collect 6 commonly-used human-annotated CRS datasets and implement 18 models that include recent techniques such as graph neural network and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to test and compare different CRS methods. The project and documents are released at https://github.com/RUCAIBox/CRSLab.