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
