用DBpedia知识增强企业表示，提升B2B客户推荐准确率
DBpedia-Enriched Company Representation for B2B Lead Recommendation
- 将DBpedia语义知识融入企业嵌入表示
- 在真实平台数据上提升推荐排序与区分能力
- 适合做企业级推荐系统优化的研究者
企业在开展企业对企业的销售时，选择目标客户是核心挑战，常依赖人工调研和零散信息。现代B2B销售平台通过集中管理企业资料，并利用学习得到的企业嵌入来支持客户推荐与优先级排序。本文研究在真实部署的集成架构中，是否通过引入来自DBpedia的语义知识，能够改善企业嵌入的下游交互预测性能。该架构融合结构化属性与文本嵌入，在真实平台用户反馈数据上评估嵌入效果。结果表明，使用DBpedia增强后的嵌入显著提升下游任务表现，尤其在排名与判别指标上均取得增益。
原文摘要 · Abstract (English)
Selecting which companies to approach is a central challenge in business-to-business (B2B) sales, where decisions are often based on manual research and fragmented information sources. Modern B2B sales platforms centralize company records and use learned company embeddings to support tasks such as recommending and prioritizing potential clients. In this study, we investigate whether enriching these company embeddings with Semantic knowledge from DBpedia improves downstream interaction-prediction performance, within a pipeline that integrates structured company attributes and text embeddings deployed on a real B2B platform. We evaluate the learned embeddings on a downstream interaction prediction task using real user feedback data from the platform. Results show that DBpedia enrichment improves downstream performance, with gains observed on ranking and discrimination metrics.
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