mlsanityData to 14 Oct 2026

TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

arXiv 2307.10543 ↗First posted 20 Jul 2023cs.AIcited by 15

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Abstract (arXiv)

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 27 of its 33 references that are in the map. First posted on arXiv 20 Jul 2023.

Builds on

27 of 33 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  4. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  5. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
  6. 2018Towards Deep Conversational Recommendationscited by 176
  7. 2016A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialoguescited by 174
  8. 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
  9. 2018Conversational Recommender Systemcited by 115
  10. 2019Towards Knowledge-Based Recommender Dialog Systemcited by 113
  11. 2020Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systemscited by 98
  12. 2020Towards Topic-Guided Conversational Recommender Systemcited by 94
15 more
  1. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 86
  2. 2020Interactive Path Reasoning on Graph for Conversational Recommendationcited by 72
  3. 2021Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learningcited by 51
  4. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  5. 2021DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendationcited by 46
  6. 2020CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendationcited by 31
  7. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29
  8. 2020Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descentcited by 26
  9. 2021Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Informationcited by 26
  10. 2022C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender Systemcited by 24
  11. 2021Learning Neural Templates for Recommender Dialogue Systemcited by 22
  12. 2021Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendationcited by 20
  13. 2022Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemblecited by 14
  14. 2023A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trendscited by 8
  15. 2022Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognitioncited by 7

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Cited by year (2026 to 14 Oct)

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

15 papers.

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  1. 2024medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRscited by 16
  2. 2023MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Controlcited by 11
  3. 2024Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factorscited by 9
  4. 2024Towards Empathetic Conversational Recommender Systemscited by 9
  5. 2024KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregationcited by 7
  6. 2025Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated Usercited by 5
  7. 2023A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systemscited by 3
  8. 2025Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answeringcited by 3
  9. 2024Personalized Topic Selection Model for Topic-Grounded Dialoguecited by 1
  10. 2025UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generationcited by 1
  11. 2025Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Expertscited by 1
  12. 2026User Simulator-Guided Multi-Turn Preference Optimization for Reasoning LLM-based Conversational Recommendationcited by 1
3 more
  1. 2026TRACE: Tourism Recommendation with Accountable Citation Evidencecited by 1
  2. 2025LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systemscited by 0
  3. 2025Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Modelscited by 0