# Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation

arXiv: https://arxiv.org/abs/2212.11868 · HTML: https://mlsanity.com/paper/2212.11868/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 40 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion](https://mlsanity.com/paper/2007.04032/) (2020)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [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)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [Learning to Select Knowledge for Response Generation in Dialog Systems](https://mlsanity.com/paper/1902.04911/) (2019)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Commonsense Knowledge Base Completion with Structural and Semantic Context](https://mlsanity.com/paper/1910.02915/) (2019)
- [Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning](https://mlsanity.com/paper/2105.09710/) (2021)
- [CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation](https://mlsanity.com/paper/2010.10333/) (2020)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Variational Knowledge Graph Reasoning](https://mlsanity.com/paper/1803.06581/) (2018)
- [C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System](https://mlsanity.com/paper/2201.02732/) (2022)
- [Mapping Text to Knowledge Graph Entities using Multi-Sense LSTMs](https://mlsanity.com/paper/1808.07724/) (2018)
- [Edge: Enriching Knowledge Graph Embeddings with External Text](https://mlsanity.com/paper/2104.04909/) (2021)

## Built on by (most cited first, 22 of 22)

- [How Can Recommender Systems Benefit from Large Language Models: A Survey](https://mlsanity.com/paper/2306.05817/) (2023)
- [Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2305.13112/) (2023)
- [DeepShop: A Benchmark for Deep Research Shopping Agents](https://mlsanity.com/paper/2506.02839/) (2025)
- [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024)
- [MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization](https://mlsanity.com/paper/2410.07672/) (2024)
- [Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning](https://mlsanity.com/paper/2312.05762/) (2023)
- [Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers](https://mlsanity.com/paper/2505.20128/) (2025)
- [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)
- [Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors](https://mlsanity.com/paper/2404.03304/) (2024)
- [Towards Empathetic Conversational Recommender Systems](https://mlsanity.com/paper/2409.10527/) (2024)
- [Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2405.01868/) (2024)
- [Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations](https://mlsanity.com/paper/2409.14399/) (2024)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User](https://mlsanity.com/paper/2504.20458/) (2025)
- [A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems](https://mlsanity.com/paper/2309.07682/) (2023)
- [Retrieval Augmented Conversational Recommendation with Reinforcement Learning](https://mlsanity.com/paper/2604.04457/) (2026)
- [Universal Knowledge Graph Embeddings](https://mlsanity.com/paper/2310.14899/) (2023)
- [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)
- [STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation](https://mlsanity.com/paper/2508.10669/) (2025)
- [USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model](https://mlsanity.com/paper/2509.20381/) (2025)

## Abstract (arXiv)

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.