  Recommender systems are software tools used to generate and provide
suggestions for items and other entities to the users by exploiting various
strategies. Hybrid recommender systems combine two or more recommendation
strategies in different ways to benefit from their complementary advantages.
This systematic literature review presents the state of the art in hybrid
recommender systems of the last decade. It is the first quantitative review
work completely focused in hybrid recommenders. We address the most relevant
problems considered and present the associated data mining and recommendation
techniques used to overcome them. We also explore the hybridization classes
each hybrid recommender belongs to, the application domains, the evaluation
process and proposed future research directions. Based on our findings, most of
the studies combine collaborative filtering with another technique often in a
weighted way. Also cold-start and data sparsity are the two traditional and top
problems being addressed in 23 and 22 studies each, while movies and movie
datasets are still widely used by most of the authors. As most of the studies
are evaluated by comparisons with similar methods using accuracy metrics,
providing more credible and user oriented evaluations remains a typical
challenge. Besides this, newer challenges were also identified such as
responding to the variation of user context, evolving user tastes or providing
cross-domain recommendations. Being a hot topic, hybrid recommenders represent
a good basis with which to respond accordingly by exploring newer opportunities
such as contextualizing recommendations, involving parallel hybrid algorithms,
processing larger datasets, etc.
