  Social network platforms can use the data produced by their users to serve
them better. One of the services these platforms provide is recommendation
service. Recommendation systems can predict the future preferences of users
using their past preferences. In the recommendation systems literature there
are various techniques, such as neighborhood based methods, machine-learning
based methods and matrix-factorization based methods. In this work, a set of
well known methods from natural language processing domain, namely Word2Vec, is
applied to recommendation systems domain. Unlike previous works that use
Word2Vec for recommendation, this work uses non-textual features, the
check-ins, and it recommends venues to visit/check-in to the target users. For
the experiments, a Foursquare check-in dataset is used. The results show that
use of continuous vector space representations of items modeled by techniques
of Word2Vec is promising for making recommendations.
