In this paper, we describe an embedding-based entity recommen- dation framework for Wikipedia that organizes Wikipedia into a collection of graphs layered on top of each others, learns comple- mentary 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 engage- ment. 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.