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.

翻译：在现代数字营销中，广告数据日益增长的复杂性要求智能系统能够理解产品、受众与广告内容之间的语义关系。为应对这一挑战，本文提出了一种基于知识图谱与深度学习的语义推荐数据库系统（KGSR-ADS），用于广告检索与个性化。所提出的框架集成了一个捕获多关系语义的异构广告知识图谱（Ad-KG）、一个利用大型语言模型（如GPT与LLaMA）生成上下文感知向量表示的语义嵌入层、一个推断跨实体依赖关系的图神经网络+注意力模型，以及一个基于向量索引（FAISS/Milvus）以实现高效语义搜索的数据库优化与检索层。这种分层架构既实现了精确的语义匹配，又支持可扩展的检索，从而能够在大规模异构工作负载下实现个性化的广告推荐。