  Recommending venues plays a critical rule in satisfying users' needs on
location-based social networks. Recent studies have explored the idea of
adopting collaborative ranking (CR) for recommendation, combining the idea of
learning to rank and collaborative filtering. However, CR suffers from the
sparsity problem, mainly because it associates similar users based on exact
matching of the venues in their check-in history. Even though research in
collaborative filtering has shown that considering auxiliary information such
as geographical influence, helps the model to alleviate the sparsity problem,
the same direction still needs to be explored in CR. In this work, we present a
CR framework that focuses on the top of the ranked list while integrating an
arbitrary number of similarity functions between venues as it learns the
model's parameters. We further introduce three example similarity measures
based on venues' contents and locations. Incorporating cross-venue similarity
measures into the model enhances the latent associations between users as
similar venues are also taken into account while associating users with each
other. Our experiments on the TREC Contextual Suggestion dataset show that our
proposed CR model beats other state-of-the-art venue suggestion methods.
