A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization

TL;DR

This paper introduces a comprehensive system combining knowledge graphs, deep learning, and vector search to improve semantic advertisement retrieval and personalization in digital marketing.

Contribution

It presents an integrated framework that utilizes a heterogeneous knowledge graph, large language models, and graph neural networks for enhanced semantic ad recommendation.

Findings

Effective semantic matching for ads achieved

Scalable retrieval with vector indexing demonstrated

Personalized recommendations improved in experiments

Abstract

In modern digital marketing, the growing complexity of advertisement data demands intelligent systems capable of understanding semantic relationships among products, audiences, and advertising content. To address this challenge, this paper proposes a Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System (KGSR-ADS) for advertisement retrieval and personalization. The proposed framework integrates a heterogeneous Ad-Knowledge Graph (Ad-KG) that captures multi-relational semantics, a Semantic Embedding Layer that leverages large language models (LLMs) such as GPT and LLaMA to generate context-aware vector representations, a GNN + Attention Model that infers cross-entity dependencies, and a Database Optimization & Retrieval Layer based on vector indexing (FAISS/Milvus) for efficient semantic search. This layered architecture enables both accurate semantic matching and scalable retrieval, allowing personalized ad recommendations under large-scale heterogeneous workloads.