Multi-view user representation learning for user matching without
  personal information
Hongliu Cao Ilias El Baamrani Eoin Thomas
As the digitization of travel industry accelerates, analyzing and
understanding travelers' behaviors becomes increasingly important. However,
traveler data frequently exhibit high data sparsity due to the relatively low
frequency of user interactions with travel providers. Compounding this effect
the multiplication of devices, accounts and platforms while browsing travel
products online also leads to data dispersion. To deal with these challenges,
probabilistic traveler matching can be used. Most existing solutions for user
matching are not suitable for traveler matching as a traveler's browsing
history is typically short and URLs in the travel industry are very
heterogeneous with many tokens. To deal with these challenges, we propose the
similarity based multi-view information fusion to learn a better user
representation from URLs by treating the URLs as multi-view data. The
experimental results show that the proposed multi-view user representation
learning can take advantage of the complementary information from different
views, highlight the key information in URLs and perform significantly better
than other representation learning solutions for the user matching task.