mlsanityData to 14 Oct 2026

Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation

arXiv 2310.16738 ↗First posted 25 Oct 2023cs.CL cs.IRcited by 11

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Abstract (arXiv)

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions, including selection bias and multiple popularity bias variants. Drawing inspiration from the success of generative data via using language models and data augmentation techniques, we present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model performance while mitigating biases. Through extensive experiments on ReDial and TG-ReDial benchmark datasets, we show a consistent improvement of CRS techniques with our data augmentation approaches and offer additional insights on addressing multiple newly formulated biases.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

24 of 39 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  5. 2018Self-Attentive Sequential Recommendationcited by 1,184
  6. 2018Towards Deep Conversational Recommendationscited by 176
  7. 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
  8. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  9. 2018Conversational Recommender Systemcited by 115
  10. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 113
  11. 2022A Survey on the Fairness of Recommender Systemscited by 102
  12. 2020Towards Topic-Guided Conversational Recommender Systemcited by 94
12 more
  1. 2022Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasetscited by 54
  2. 2020Correcting for Selection Bias in Learning-to-rank Systemscited by 35
  3. 2021CRSLab: An Open-Source Toolkit for Building Conversational Recommender Systemcited by 34
  4. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  5. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29
  6. 2021Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendationcited by 23
  7. 2021CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Modelscited by 23
  8. 2022The Unfairness of Popularity Bias in Book Recommendationcited by 14
  9. 2022Quantifying and Mitigating Popularity Bias in Conversational Recommender Systemscited by 11
  10. 2022Conversational Recommendation: A Grand AI Challengecited by 9
  11. 2019Quantitative analysis of Matthew effect and sparsity problem of recommender systemscited by 7
  12. 2022Meta Policy Learning for Cold-Start Conversational Recommendationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 1.

11 papers.

  1. 2024Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Eracited by 98
  2. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 18
  3. 2024Large Language Models Still Exhibit Bias in Long Textcited by 16
  4. 2025Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentationcited by 5
  5. 2025Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Makingcited by 4
  6. 2024A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systemscited by 3
  7. 2024The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judgescited by 2
  8. 2025Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusingcited by 2
  9. 2026Fairness Aware Reward Optimizationcited by 1
  10. 2024Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groupscited by 0
  11. 2025USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Modelcited by 0