  Sparsity of the User-POI matrix is a well established problem for next POI
recommendation, which hinders effective learning of user preferences. Focusing
on a more granular extension of the problem, we propose a Joint Triplet Loss
Learning (JTLL) module for the Next New ($N^2$) POI recommendation task, which
is more challenging. Our JTLL module first computes additional training samples
from the users' historical POI visit sequence, then, a designed triplet loss
function is proposed to decrease and increase distances of POI and user
embeddings based on their respective relations. Next, the JTLL module is
jointly trained with recent approaches to additionally learn unvisited
relations for the recommendation task. Experiments conducted on two known
real-world LBSN datasets show that our joint training module was able to
improve the performances of recent existing works.
