# Learning to Ask Appropriate Questions in Conversational Recommendation

arXiv: https://arxiv.org/abs/2105.04774 · HTML: https://mlsanity.com/paper/2105.04774/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 15 of 15)

- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [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)
- [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://mlsanity.com/paper/1906.05237/) (2019)
- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Try This Instead: Personalized and Interpretable Substitute Recommendation](https://mlsanity.com/paper/2005.09344/) (2020)
- [Towards Question-based Recommender Systems](https://mlsanity.com/paper/2005.14255/) (2020)
- [Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation](https://mlsanity.com/paper/1904.12796/) (2019)
- [Conversational Product Search Based on Negative Feedback](https://mlsanity.com/paper/1909.02071/) (2019)

## Built on by (most cited first, 30 of 35)

- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024)
- [Proactive Conversational Agents with Inner Thoughts](https://mlsanity.com/paper/2501.00383/) (2024)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Learning Neural Templates for Recommender Dialogue System](https://mlsanity.com/paper/2109.12302/) (2021)
- [RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models](https://mlsanity.com/paper/2110.07477/) (2021)
- [Teaching Language Models To Gather Information Proactively](https://mlsanity.com/paper/2507.21389/) (2025)
- [ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation](https://mlsanity.com/paper/2509.21730/) (2025)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (2022)
- [LLAMAPIE: Proactive In-Ear Conversation Assistants](https://mlsanity.com/paper/2505.04066/) (2025)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [Closed-book Question Generation via Contrastive Learning](https://mlsanity.com/paper/2210.06781/) (2022)
- [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022)
- [Multi-grained Hypergraph Interest Modeling for Conversational Recommendation](https://mlsanity.com/paper/2305.04798/) (2023)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models](https://mlsanity.com/paper/2504.12313/) (2025)
- [Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System](https://mlsanity.com/paper/2507.02000/) (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)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Synthetic Clarification and Correction Dialogues about Data-Centric Tasks -- A Teacher-Student Approach](https://mlsanity.com/paper/2503.14167/) (2025)
- [TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding](https://mlsanity.com/paper/2509.14671/) (2025)
- [Unified Question Generation with Continual Lifelong Learning](https://mlsanity.com/paper/2201.09696/) (2022)
- [Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge](https://mlsanity.com/paper/2209.11386/) (2022)
- [Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation](https://mlsanity.com/paper/2304.09093/) (2023)
- [Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization](https://mlsanity.com/paper/2305.03987/) (2023)
- [TRACE: A Conversational Framework for Sustainable Tourism Recommendation with Agentic Counterfactual Explanations](https://mlsanity.com/paper/2604.14223/) (2026)
- [HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation](https://mlsanity.com/paper/2607.17461/) (2026)
- [Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation](https://mlsanity.com/paper/2607.18609/) (2026)

## Abstract (arXiv)

Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user preference. In a typical conversational recommendation scenario, a CRS firstly generates questions to let the user clarify her/his demands and then makes suitable recommendations. Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations. However, existing CRSs fall short in asking high-quality questions because: (1) system-generated responses heavily depends on the performance of the dialogue policy agent, which has to be trained with huge conversation corpus to cover all circumstances; and (2) current CRSs cannot fully utilize the learned latent user profiles for generating appropriate and personalized responses. To mitigate these issues, we propose the Knowledge-Based Question Generation System (KBQG), a novel framework for conversational recommendation. Distinct from previous conversational recommender systems, KBQG models a user's preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph (KG). Conditioned on the varied importance of different relations, the generated clarifying questions could perform better in impelling users to provide more details on their preferences. Finially, accurate recommendations can be generated in fewer conversational turns. Furthermore, the proposed KBQG outperforms all baselines in our experiments on two real-world datasets.