  A context-aware recommender system (CARS) applies sensing and analysis of
user context to provide personalized services. The contextual information can
be driven from sensors in order to improve the accuracy of the recommendations.
Yet, generating accurate recommendations is not enough to constitute a useful
system from the users' perspective, since certain contextual information may
cause different issues, such as draining the user's battery, privacy issues,
and more. Adding high-dimensional contextual information may increase both the
dimensionality and sparsity of the model. Previous studies suggest reducing the
amount of contextual information by selecting the most suitable contextual
information using a domain knowledge. Another solution is compressing it into a
denser latent space, thus disrupting the ability to explain the recommendation
item to the user, and damaging users' trust. In this paper we present an
approach for selecting low-dimensional subsets of the contextual information
and incorporating them explicitly within CARS. Specifically, we present a novel
feature-selection algorithm, based on genetic algorithms (GA), that outperforms
SOTA dimensional-reduction CARS algorithms, improves the accuracy and the
explainability of the recommendations, and allows for controlling user aspects,
such as privacy and battery consumption. Furthermore, we exploit the top
subsets that are generated along the evolutionary process, by learning multiple
deep context-aware models and applying a stacking technique on them, thus
improving the accuracy while remaining at the explicit space. We evaluated our
approach on two high-dimensional context-aware datasets driven from
smartphones. An empirical analysis of our results validates that our proposed
approach outperforms SOTA CARS models while improving transparency and
explainability to the user.
