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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.

The paper summarizes the results of several industrial surveys on issues related to the development of systems using Commercial-Off-The-Shelf and Open Source Software components. The results demonstrate the following. (1) There is a discrepancy between academic theory and industrial practices regarding the use of components. One reason is that researchers have empirically evaluated only a few theoretical methods; hence, industrial practitioners currently have no reason to adopt them. Another reason might be that researchers have specified the contexts of application of only a small number of theories in sufficient detail to avoid misleading users. (2) Academic researchers often hold false assumptions about industry. For example, research on requirement negotiations often assumes that a client will be interested in, and be capable of, discussing the technical details of a project. However, in practice this is usually not true. In addition, the quality of a component in the final system is often attributed solely to component quality before integration, ignoring quality improvements by integrators during component integration.