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Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

arXiv 2007.04032 ↗First posted 8 Jul 2020cs.CL cs.AI cs.IRcited by 122

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Abstract (arXiv)

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference. To address these issues, we incorporate both word-oriented and entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization to align the word-level and entity-level semantic spaces. Based on the aligned semantic representations, we further develop a KG-enhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive experiments have demonstrated the effectiveness of our approach in yielding better performance on both recommendation and conversation tasks.

Key facts. Cited by 122 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2020.

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Citing papers per year: 2021: 17; 2022: 26; 2023: 31; 2024: 19; 2025: 18; 2026: 11.

122 papers.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  2. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 141
  3. 2023Large Language Models as Zero-Shot Conversational Recommenderscited by 126
  4. 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
  5. 2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
  6. 2022Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learningcited by 85
  7. 2022Personalized Prompt Learning for Explainable Recommendationcited by 79
  8. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  9. 2021Personalized Transformer for Explainable Recommendationcited by 58
  10. 2023Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Modelscited by 48
  11. 2021RevCore: Review-augmented Conversational Recommendationcited by 47
  12. 2024A Multi-Agent Conversational Recommender Systemcited by 42
38 more
  1. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  2. 2021Learning to Ask Appropriate Questions in Conversational Recommendationcited by 35
  3. 2021CRSLab: An Open-Source Toolkit for Building Conversational Recommender Systemcited by 34
  4. 2021Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representationcited by 32
  5. 2022A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systemscited by 32
  6. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  7. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  8. 2022User-Centric Conversational Recommendation with Multi-Aspect User Modelingcited by 29
  9. 2021Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Informationcited by 26
  10. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
  11. 2022C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender Systemcited by 24
  12. 2021Learning Neural Templates for Recommender Dialogue Systemcited by 22
  13. 2022Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendationcited by 22
  14. 2024A Survey of Conversational Searchcited by 22
  15. 2022BARCOR: Towards A Unified Framework for Conversational Recommendation Systemscited by 21
  16. 2022Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphscited by 21
  17. 2022CausPref: Causal Preference Learning for Out-of-Distribution Recommendationcited by 17
  18. 2025A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigmscited by 17
  19. 2023TREA: Tree-Structure Reasoning Schema for Conversational Recommendationcited by 15
  20. 2024Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Datasetcited by 15
  21. 2021Efficient Non-Sampling Knowledge Graph Embeddingcited by 14
  22. 2021KECRS: Towards Knowledge-Enriched Conversational Recommendation Systemcited by 14
  23. 2021RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Modelscited by 14
  24. 2021Recall and Learn: A Memory-augmented Solver for Math Word Problemscited by 13
  25. 2022Comparison-based Conversational Recommender System with Relative Bandit Feedbackcited by 12
  26. 2023U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendationcited by 12
  27. 2024A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systemscited by 12
  28. 2021DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generationcited by 11
  29. 2023Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentationcited by 11
  30. 2024Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systemscited by 11
  31. 2025RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systemscited by 11
  32. 2022Hierarchical Conversational Preference Elicitation with Bandit Feedbackcited by 10
  33. 2023Improving Conversational Recommendation Systems via Counterfactual Data Simulationcited by 10
  34. 2024Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendationcited by 10
  35. 2025Graph Retrieval-Augmented LLM for Conversational Recommendation Systemscited by 10
  36. 2024Parameter-Efficient Conversational Recommender System as a Language Processing Taskcited by 9
  37. 2024Continual Learning for Smart City: A Surveycited by 9
  38. 2024Towards Empathetic Conversational Recommender Systemscited by 9

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