Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph
This work provides a practical solution for entity recommendation in Wikipedia, integrated into the Yahoo! Knowledge Graph, but it is incremental as it builds on existing embedding and graph-based methods.
The paper tackles the problem of recommending related entities on Wikipedia by developing an embedding-based framework that layers graphs and combines topology and content representations, achieving good quality and user engagement in evaluations.
In this paper, we describe an embedding-based entity recommendation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each other, learns complementary entity representations from their topology and content, and combines them with a lightweight learning-to-rank approach to recommend related entities on Wikipedia. Through offline and online evaluations, we show that the resulting embeddings and recommendations perform well in terms of quality and user engagement. Balancing simplicity and quality, this framework provides default entity recommendations for English and other languages in the Yahoo! Knowledge Graph, which Wikipedia is a core subset of.