  Venue recommendation aims to assist users by making personalised suggestions
of venues to visit, building upon data available from location-based social
networks (LBSNs) such as Foursquare. A particular challenge for this task is
context-aware venue recommendation (CAVR), which additionally takes the
surrounding context of the user (e.g. the user's location and the time of day)
into account in order to provide more relevant venue suggestions. To address
the challenges of CAVR, we describe two approaches that exploit word embedding
techniques to infer the vector-space representations of venues, users' existing
preferences, and users' contextual preferences. Our evaluation upon the test
collection of the TREC 2015 Contextual Suggestion track demonstrates that we
can significantly enhance the effectiveness of a state-of-the-art venue
recommendation approach, as well as produce context-aware recommendations that
are at least as effective as the top TREC 2015 systems.
