  Finding the next venue to be visited by a user in a specific city is an
interesting, but challenging, problem. Different techniques have been proposed,
combining collaborative, content, social, and geographical signals; however it
is not trivial to decide which tech- nique works best, since this may depend on
the data density or the amount of activity logged for each user or item. At the
same time, cross-domain strategies have been exploited in the recommender
systems literature when dealing with (very) sparse situations, such as those
inherently arising when recommendations are produced based on information from
a single city.
  In this paper, we address the problem of venue recommendation from a novel
perspective: applying cross-domain recommenda- tion techniques considering each
city as a different domain. We perform an experimental comparison of several
recommendation techniques in a temporal split under two conditions:
single-domain (only information from the target city is considered) and cross-
domain (information from many other cities is incorporated into the
recommendation algorithm). For the latter, we have explored two strategies to
transfer knowledge from one domain to another: testing the target city and
training a model with information of the k cities with more ratings or only
using the k closest cities.
  Our results show that, in general, applying cross-domain by proximity
increases the performance of the majority of the recom- menders in terms of
relevance. This is the first work, to the best of our knowledge, where so many
domains (eight) are combined in the tourism context where a temporal split is
used, and thus we expect these results could provide readers with an overall
picture of what can be achieved in a real-world environment.
