Context-Aware E-Commerce Recommendation via Knowledge Graph- Enriched Graph Neural Networks

We chose a knowledge-graph-plus-GNN approach
specifically because it addresses this gap without abandoning
collaborative signals altogether. Rather than replacing user-
item interaction data with content-based rules, the proposed
pipeline keeps the interaction graph as the backbone and
augments it with structured attribute edges (Item–Brand,
Item–Category), allowing the GNN to propagate preference
signal along attribute pathways that a plain bipartite model
cannot see. Neo4j was chosen as the underlying store because
it allows this structure to be expressed, queried, and inspected
directly as a graph via Cypher and the Graph Data Science
library rather than reconstructed implicitly inside a black-box
model, which also makes the resulting recommendations
easier to explain to a non-technical stakeholder (Section V-E
gives a concrete example of this). Critically, we did not
simply assume that adding this context would help; the
ablation design in Section V-E was built specifically to test
that assumption and report an honest, attributable answer
rather than an unverified claim.