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This paper focuses on developing a method of hybrid context-aware recommender systems, based on ontologies in the environment of the Spatial Data Infrastructure, which will positively impact the effectiveness of decision-making in the tourist applications for mobile users. The method developed has three components: data and semantic framework, other that implements the recommender system and another responsible for displaying the recommendations. The recommender system component implements a spatial, a semantic and collaborative filters. The recommendations generated show the elements of potential interest, these can be displayed in tabular or combined with spatial information. It was found through an experiment and different applied metrics the increase in its effectiveness for mobile users according to their preferences as to the recommendations given in a spatial environment. [ABSTRACT FROM AUTHOR]

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In the current techno-social environment the importance of providing personalized content for users of social platforms, continually adapting to their needs, is key to the success of the existing platforms. Recommender systems play a crucial role in this regard, but in many cases the level of customization provided is not enough. This article discusses two key aspects related to the development of recommender systems: proactivity and context-awareness. We propose a theoretical reference model for the creation of proactive recommender systems based on context-awareness information. In addition, we check its feasibility in two real-world scenarios where the model has been successfully implemented: banking and personal learning networks. Finally, future courses of action following the contributions proposed are highlighted, with special attention to potential applications in educational environments. [ABSTRACT FROM AUTHOR]