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Semantic trajectories are high level representations of user movements whereseveral aspects related to the movement context are represented asheterogeneous textual labels. With the objective of finding a meaningfulsimilarity measure for semantically enriched trajectories, we proposeTraj2User, a Word2Vec-inspired method for the generation of a vectorrepresentation of user movements as user embeddings. Traj2User uses simplerepresentations of trajectories and delegates the definition of the similaritymodel to the learning process of the network. Preliminary results show thatTraj2User is able to generate effective user embeddings.

Traj2User: exploiting embeddings for computing similarity of users mobile behavior