  Semantic trajectories are high level representations of user movements where
several aspects related to the movement context are represented as
heterogeneous textual labels. With the objective of finding a meaningful
similarity measure for semantically enriched trajectories, we propose
Traj2User, a Word2Vec-inspired method for the generation of a vector
representation of user movements as user embeddings. Traj2User uses simple
representations of trajectories and delegates the definition of the similarity
model to the learning process of the network. Preliminary results show that
Traj2User is able to generate effective user embeddings.
