Towards Knowledge-Based Recommender Dialog System

2019/08/15 by Qibin Chen, Junyang Lin, Chen, Qibin +11 · 22 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Recommender Systems and Techniques #Topic Modeling #cs.CL #cs.IR #cs.LG

paper · pdf · doi:10.48550/arxiv.1908.05391

To appear in EMNLP 2019

openalex publication_date 2019/08/15 · arxiv created 2019/09/03 · arxiv updated 2019/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog generation system. The dialog system can enhance the performance of the recommendation system by introducing knowledge-grounded information about users' preferences, and the recommender system can improve that of the dialog generation system by providing recommendation-aware vocabulary bias. Experimental results demonstrate that our proposed model has significant advantages over the baselines in both the evaluation of dialog generation and recommendation. A series of analyses show that the two systems can bring mutual benefits to each other, and the introduced knowledge contributes to both their performances.

Citations

• A Neural Conversational Model
• Sequence to Sequence Learning with Neural Networks
• Towards Deep Conversational Recommendations
• Deep Conversational Recommender in Travel

Cited by

• UserSimCRS v2: Simulation-Based Evaluation for Conversational Recommender Systems
• Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models
• Reproducibility in Recommender Systems: A Survey
• Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
• AgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence
• Limitations of Current Evaluation Practices for Conversational Recommender Systems and the Potential of User Simulation
• Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation
• ReGeS: Reciprocal Retrieval-Generation Synergy for Conversational Recommender Systems
• USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model
• CARE: Contextual Adaptation of Recommenders for LLM-based Conversational Recommendation
• STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
• Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation
• LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Recommender Systems
• SimLab: A Platform for Simulation-based Evaluation of Conversational Information Access Systems
• Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
• On Mitigating Data Sparsity in Conversational Recommender Systems
• FACE: A Fine-grained Reference Free Evaluator for Conversational Recommender Systems
• Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models
• Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation
• From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System
• Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
• Exploring the Impact of Personality Traits on Conversational Recommender Systems: A Simulation with Large Language Models

Related

• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
• KECRS: Towards Knowledge-Enriched Conversational Recommendation System
• Explicit Knowledge Graph Reasoning for Conversational Recommendation
• Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information
• Leveraging Knowledge Graph Embedding for Effective Conversational Recommendation