Abstract

In a Web dominated by social media for information, relationships and communication, the established dynamics between content, people and technology change radically. Given the relevance of the user-generated content in such a scenario and its essentially relational nature, successfully locating the best sources of information requires the development of recommender systems that incorporate the social characteristic of scenarios built on a network that goes beyond the original Internet. The article provides a review of current approaches to the process of recommendation, placing them in the context of consolidated trends associated with the phenomenon of Social Computing, and highlights some lines of development in the area of redefining the problem of recommendation in a context dominated by social networks and user-generated content.

Abstract

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.