  As the popularity of Location-based Social Networks (LBSNs) increases,
designing accurate models for Point-of-Interest (POI) recommendation receives
more attention. POI recommendation is often performed by incorporating
contextual information into previously designed recommendation algorithms. Some
of the major contextual information that has been considered in POI
recommendation are the location attributes (i.e., exact coordinates of a
location, category, and check-in time), the user attributes (i.e., comments,
reviews, tips, and check-in made to the locations), and other information, such
as the distance of the POI from user's main activity location, and the social
tie between users. The right selection of such factors can significantly impact
the performance of the POI recommendation. However, previous research does not
consider the impact of the combination of these different factors. In this
paper, we propose different contextual models and analyze the fusion of
different major contextual information in POI recommendation. The major
contributions of this paper are: (i) providing an extensive survey of
context-aware location recommendation (ii) quantifying and analyzing the impact
of different contextual information (e.g., social, temporal, spatial, and
categorical) in the POI recommendation on available baselines and two new
linear and non-linear models, that can incorporate all the major contextual
information into a single recommendation model, and (iii) evaluating the
considered models using two well-known real-world datasets. Our results
indicate that while modeling geographical and temporal influences can improve
recommendation quality, fusing all other contextual information into a
recommendation model is not always the best strategy.
