用知识图谱和深度学习实现广告精准推荐与高效检索
A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization
- 构建多关系广告知识图谱，融合语义嵌入与图神经网络
- 支持大规模异构数据下个性化推荐，检索效率提升显著
- 适合广告平台、推荐系统开发者快速落地应用
在现代数字营销中，广告数据的复杂性要求智能系统理解产品、受众与广告内容间的语义关系。本文提出一种基于知识图谱与深度学习的广告检索与个性化推荐数据库系统（KGSR-ADS）。该框架集成异构广告知识图谱（Ad-KG），利用GPT、LLaMA等大语言模型生成上下文感知的向量表示，通过图神经网络与注意力机制推断跨实体依赖关系，并基于向量索引技术（FAISS/Milvus）优化数据库检索层，实现高效语义搜索。该分层架构支持在大规模异构负载下的精准语义匹配与可扩展检索，显著提升个性化广告推荐能力。
原文摘要 · Abstract (English)
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.
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