About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Chien-Chun Ni
- sl:arxiv_num : 2004.06842
- sl:arxiv_published : 2020-04-15T00:49:27Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph@en
- sl:arxiv_updated : 2020-04-15T00:49:27Z
- sl:bookmarkOf : https://arxiv.org/abs/2004.06842
- sl:creationDate : 2020-04-17
- sl:creationTime : 2020-04-17T19:14:01Z
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