Explain What You Mean: Intent Augmented Knowledge Graph Recommender Built With LLM

2025-05-16

AI Summary

To address user-item interaction sparsity and cold-start challenges in recommender systems, this paper proposes the Intent-enhanced Knowledge Graph Recommendation framework (IKGR). IKGR introduces a novel intent connectivity densification mechanism that jointly integrates retrieval-augmented generation (RAG), knowledge graph encoding, large language model (LLM)-driven intent modeling, interaction-aware graph neural networks, and embedding-space alignment translation. This yields an interpretable embedding translation layer enabling intent traceability and knowledge completion. Unlike existing approaches, IKGR requires no manually annotated external data or domain expert involvement, thereby alleviating computational bottlenecks and constraints imposed by data quality. Extensive experiments on multiple real-world datasets demonstrate that IKGR consistently outperforms state-of-the-art baselines, achieving significant improvements in recommendation accuracy—particularly under sparse interaction conditions—while enhancing model interpretability.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Interaction sparsity is the primary obstacle for recommendation systems. Sparsity manifests in environments with disproportional cardinality of groupings of entities, such as users and products in an online marketplace. It also is found for newly introduced entities, described as the cold-start problem. Recent efforts to mitigate this sparsity issue shifts the performance bottleneck to other areas in the computational pipeline. Those that focus on enriching sparse representations with connectivity data from other external sources propose methods that are resource demanding and require careful domain expert aided addition of this newly introduced data. Others that turn to Large Language Model (LLM) based recommenders will quickly encounter limitations surrounding data quality and availability. In this work, we propose LLM-based Intent Knowledge Graph Recommender (IKGR), a novel framework that leverages retrieval-augmented generation and an encoding approach to construct and densify a knowledge graph. IKGR learns latent user-item affinities from an interaction knowledge graph and further densifies it through mutual intent connectivity. This addresses sparsity issues and allows the model to make intent-grounded recommendations with an interpretable embedding translation layer. Through extensive experiments on real-world datasets, we demonstrate that IKGR overcomes knowledge gaps and achieves substantial gains over state-of-the-art baselines on both publicly available and our internal recommendation datasets.

Problem

• Addressing interaction sparsity in recommendation systems
• Overcoming cold-start problems for new entities
• Reducing resource demands in enriching sparse representations

Innovation

• LLM-based Intent Knowledge Graph Recommender (IKGR)
• Retrieval-augmented generation for knowledge graph
• Interpretable embedding translation layer