  Thanks to the advancing mobile location services, people nowadays can post
about places to share visiting experience on-the-go. A large place graph not
only helps users explore interesting destinations, but also provides
opportunities for understanding and modeling the real world. To improve
coverage and flexibility of the place graph, many platforms import places data
from multiple sources, which unfortunately leads to the emergence of numerous
duplicated places that severely hinder subsequent location-related services. In
this work, we take the anonymous place graph from Facebook as an example to
systematically study the problem of place deduplication: We carefully formulate
the problem, study its connections to various related tasks that lead to
several promising basic models, and arrive at a systematic two-step data-driven
pipeline based on place embedding with multiple novel techniques that works
significantly better than the state-of-the-art.

Computer Vision and Pattern Recognition