  As the number and variety of smart devices increase, users may use myriad
devices in their daily lives and the online activities become highly
fragmented. Building an accurate user identity becomes a difficult and
important problem for advertising companies. The task for the CIKM Cup 2016
Track 1 was to find the same user cross multiple devices. This paper discusses
our solution to the challenge. It is mainly comprised of three parts:
comprehensive feature engineering, negative sampling, and model selection. For
each part we describe our special steps and demonstrate how the performance is
boosted. We took the second prize of the competition with an F1-score of
0.41669.
