# CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation

arXiv: https://arxiv.org/abs/2010.10333 · HTML: https://mlsanity.com/paper/2010.10333/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 30 of 31)

- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information](https://mlsanity.com/paper/2112.08140/) (2021)
- [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://mlsanity.com/paper/2212.11868/) (2022)
- [TREA: Tree-Structure Reasoning Schema for Conversational Recommendation](https://mlsanity.com/paper/2307.10543/) (2023)
- [Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue](https://mlsanity.com/paper/2310.14626/) (2023)
- [Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems](https://mlsanity.com/paper/2204.00763/) (2022)
- [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)
- [Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems](https://mlsanity.com/paper/2208.03516/) (2022)
- [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022)
- [Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue](https://mlsanity.com/paper/2403.06063/) (2024)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [Bridging Conversational and Collaborative Signals for Conversational Recommendation](https://mlsanity.com/paper/2412.06949/) (2024)
- [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)
- [EGCR: Explanation Generation for Conversational Recommendation](https://mlsanity.com/paper/2208.08035/) (2022)
- [A Major Obstacle for NLP Research: Let's Talk about Time Allocation!](https://mlsanity.com/paper/2211.16858/) (2022)
- [Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation](https://mlsanity.com/paper/2304.09093/) (2023)
- [Conversational Recommendation as Retrieval: A Simple, Strong Baseline](https://mlsanity.com/paper/2305.13725/) (2023)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (2025)
- [Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues](https://mlsanity.com/paper/2605.20195/) (2026)
- [Chat, Shift and Perform: Bridging the Gap between Task-oriented and Non-task-oriented Dialog Systems](https://mlsanity.com/paper/2206.11813/) (2022)
- [Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System](https://mlsanity.com/paper/2303.00311/) (2023)
- [Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples](https://mlsanity.com/paper/2303.05575/) (2023)
- [CoRE-CoG: Conversational Recommendation of Entities using Constrained Generation](https://mlsanity.com/paper/2311.08511/) (2023)
- [Large Language Model Driven Recommendation](https://mlsanity.com/paper/2408.10946/) (2024)
- [LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems](https://mlsanity.com/paper/2507.04722/) (2025)
- [SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization](https://mlsanity.com/paper/2604.26394/) (2026)

## Abstract (arXiv)

Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation. To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. The unique scheme of tree-structured reasoning views the traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.