Towards Knowledge-Based Recommender Dialog System

2019/08/15 by Chen, Qibin, Lin, Junyang, Zhang, Yichang +4 · 12 citations
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.1908.05391

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.

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

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