Recommendation Systems that Establish New Forms of Representational Reality through Journalistic Content


The abstract aims to connect various fields of knowledge, including journalism, information sciences, and human cognition. It analyzes Recommendation Systems (RS) on the Web in order to understand how they are changing user-experiences and, consequently, affecting the fundamental structures of journalism. Today’s networks are decentralized with few barriers to entry. They provide new ways of consuming journalistic production, and have altered the system established by broadcasting, transforming the relationship between audience and informational preferences. New possibilities are emerging that will define how the information emitter provides social relevance to its audience. Relevance is the basis of RS and for the algorithms that, unseen by their audience, operate invisibly and transparently. New information ecosystem is eliminating the dividing line between information emitter and receptor of journalistic information. This represents a unique visualization and availability context that does not differentiate between direct human actions and the actions of computer programmed algorithms.

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