Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation

Abstract

Conversational recommender systems (CRSs) often utilize external knowledge graphs (KGs) to introduce rich semantic information and recommend relevant items through natural language dialogues. However, original KGs employed in existing CRSs are often incomplete and sparse, which limits the reasoning capability in recommendation. Moreover, only few of existing studies exploit the dialogue context to dynamically refine knowledge from KGs for better recommendation. To address the above issues, we propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR). Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs; and perform dynamic knowledge reasoning conditioned on the dialogue context. Specifically, we denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs refactor. We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context. Finally, we infuse the dialogue-specific subgraphs to decode the recommendation and responses. We conduct experiments on two benchmark CRSs datasets. Experimental results confirm the effectiveness of our proposed method.

References in corpus (6)

• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
• Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
• Interactive Path Reasoning on Graph for Conversational Recommendation
• Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
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• C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

Cited by in corpus (7)

• LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models
• Towards Empathetic Conversational Recommender Systems
• Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling
• MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems
• Universal Knowledge Graph Embeddings
• STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation