mlsanityData to 7 Oct 2026

BARCOR: Towards A Unified Framework for Conversational Recommendation Systems

arXiv 2203.14257 ↗First posted 27 Mar 2022cs.CLcited by 21

Not on the map. Why?

Abstract (arXiv)

Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by the past observed behaviors. In contrast, conversational recommendation systems (CRS) aim to understand users' preferences via interactions in conversation flows. CRS is a complex problem that consists of two main tasks: (1) recommendation and (2) response generation. Previous work often tried to solve the problem in a modular manner, where recommenders and response generators are separate neural models. Such modular architectures often come with a complicated and unintuitive connection between the modules, leading to inefficient learning and other issues. In this work, we propose a unified framework based on BART for conversational recommendation, which tackles two tasks in a single model. Furthermore, we also design and collect a lightweight knowledge graph for CRS in the movie domain. The experimental results show that the proposed methods achieve the state-of-the-art performance in terms of both automatic and human evaluation.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 16 of its 26 references that are in the map. First posted on arXiv 27 Mar 2022.

Builds on

16 of 26 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  5. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  6. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  7. 2018Towards Deep Conversational Recommendationscited by 174
  8. 2018RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systemscited by 136
  9. 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
  10. 2018Conversational Recommender Systemcited by 115
  11. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 112
  12. 2015A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestioncited by 68
4 more
  1. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  2. 2019Conversational Contextual Bandit: Algorithm and Applicationcited by 35
  3. 2020Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Userscited by 33
  4. 2021KECRS: Towards Knowledge-Enriched Conversational Recommendation Systemcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 7; 2025: 5; 2026: 2.

21 papers.

Sort:

By area:

  1. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  2. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendationscited by 130
  3. 2023When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunitiescited by 89
  4. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  5. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  6. 2024A Multi-Agent Conversational Recommender Systemcited by 42
  7. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
  8. 2024A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systemscited by 12
  9. 2023Improving Conversational Recommendation Systems via Counterfactual Data Simulationcited by 10
  10. 2025Graph Retrieval-Augmented LLM for Conversational Recommendation Systemscited by 10
  11. 2024Parameter-Efficient Conversational Recommender System as a Language Processing Taskcited by 9
  12. 2024Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factorscited by 9
9 more
  1. 2024Beyond Persuasion: Towards Conversational Recommender System with Credible Explanationscited by 7
  2. 2023Multi-grained Hypergraph Interest Modeling for Conversational Recommendationcited by 5
  3. 2024Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendationscited by 4
  4. 2025Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentationcited by 4
  5. 2025Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Expertscited by 4
  6. 2026HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendationcited by 2
  7. 2025USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Modelcited by 0
  8. 2025Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Modelscited by 0
  9. 2026Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Searchcited by 0