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.
- Pub Date:
- April 2020
- Computer Science - Information Retrieval;
- Computer Science - Machine Learning;
- Computer Science - Social and Information Networks;
- 8 pages, 4 figures, 8 tables. To be appeared in Wiki Workshop 2020, Companion Proceedings of the Web Conference 2020(WWW 20 Companion), Taipei, Taiwan