DBpedia-Enriched Company Representation for B2B Lead Recommendation
For B2B sales platforms, this work shows a modest improvement by adding external knowledge to company embeddings.
The paper investigates whether enriching company embeddings with DBpedia semantic knowledge improves B2B lead recommendation, finding that it yields gains in ranking and discrimination metrics on a real platform.
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.