Los retos de la recomendación de contenido audiovisual

In this article, we use the term recommendation in order to analyze every apparatus aimed at guiding the Internet user toward a specific content, surrounded by an overabundant and profuse offer. For the purpose of the analysis, we distinguish four types of recommendation: on the one hand, editorial and contributive recommendation, based either on experts or users’ judgments and opinions, and on the other hand, aggregative and personalized. The latter appear to be more and more central, based on users’ online behaviour. With the algorithmic recommendation, alleged, declared or observed users’ preferences form the material of predictive recommendation in apparatuses developed by different operators of legacy media. This step is the almost final one in a process of delinearization and individualization. However, despite its efficiency, this form of recommendation raises a number of questions. More than the risk of confinement and filter bubble, the biggest problem seems to be related to the oligopolistic power of a few players, controlling consumer behaviour data