Knowledge Graph Embedding for Intelligent Recommendation in Smart Retail Systems

Abstract

Retail catalogs are naturally structured as knowledge graphs: products connect to categories, brands, attributes, and to one
another through co-purchase and substitution relations that carry far more signal than an interaction matrix alone. This
article synthesizes the literature on knowledge graph embedding (KGE) for intelligent recommendation in smart retail systems,
tracing the technique from foundational translation-based and semantic-matching embedding models through
recommendation-specific architectures that fuse a knowledge graph with collaborative filtering signal via preference
propagation, multi-task feature sharing, and graph neural attention. We give particular weight to the systems literature on
actually constructing and maintaining a retail-scale product knowledge graph, including large industrial pipelines that
automatically collect billions of product facts across tens of thousands of product types, since the sophistication of the
downstream recommender is bounded by the quality of the graph it embeds. Read together, the evidence supports
knowledge-graph-embedding-based recommendation as a coherent and increasingly well-evidenced pattern for addressing
sparsity, cold start, and explainability limitations in retail recommendation, while leaving retail-native benchmarking,
explanation validation, and the reliability of automatically extracted product facts as substantially open problems.