  The recommendation of points of interest (POIs) is essential in
location-based social networks. It makes it easier for users and locations to
share information. Recently, researchers tend to recommend POIs by treating
them as large-scale retrieval systems that require a large amount of training
data representing query-item relevance. However, gathering user feedback in
retrieval systems is an expensive task. Existing POI recommender systems make
recommendations based on user and item (location) interactions solely. However,
there are numerous sources of feedback to consider. For example, when the user
visits a POI, what is the POI is about and such. Integrating all these
different types of feedback is essential when developing a POI recommender. In
this paper, we propose using user and item information and auxiliary
information to improve the recommendation modelling in a retrieval system. We
develop a deep neural network architecture to model query-item relevance in the
presence of both collaborative and content information. We also improve the
quality of the learned representations of queries and items by including the
contextual information from the user feedback data. The application of these
learned representations to a large-scale dataset resulted in significant
improvements.
