The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems

There is a widespread myth and rhetoric, even in academic discourse, about data and VOD recommender systems, especially with regard to the notion of automation and the innocence of this presumed automation. Behind this rhetoric lies the de-humanization of machine computation, i.e. the remov...

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Main Author: Giorgio Avezzù
Format: Article
Language:English
Published: Milano University Press 2017-10-01
Series:Cinéma & Cie
Online Access:https://riviste.unimi.it/index.php/cinemaetcie/article/view/16569
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author Giorgio Avezzù
author_facet Giorgio Avezzù
author_sort Giorgio Avezzù
collection DOAJ
description There is a widespread myth and rhetoric, even in academic discourse, about data and VOD recommender systems, especially with regard to the notion of automation and the innocence of this presumed automation. Behind this rhetoric lies the de-humanization of machine computation, i.e. the removal of all the processual, decisional, ‘oriented’ aspects informing every online recommender system. This essay focuses on content-to-content video recommendations, which are based on patterns of similarity between different contents, and it intends to show that there is nothing neutral — even in the most seemingly ‘objective’ form of video recommendation. The aim is to rediscover those very processual elements of the ‘data supply chain’ — regarding how metadata are created and collected, and how algorithms are con gured — so as to make them critically observable again: the funnels, decision points, the multiple layers of human mediation and ltering, in both their relevance and sensitivity.
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spelling doaj.art-a3deee61aed54909926ef919c832bb3b2024-02-15T15:28:16ZengMilano University PressCinéma & Cie2036-461X2017-10-011729The Data Don’t Speak for Themselves: The Humanity of VOD Recommender SystemsGiorgio Avezzù0Università Cattolica del Sacro Cuore There is a widespread myth and rhetoric, even in academic discourse, about data and VOD recommender systems, especially with regard to the notion of automation and the innocence of this presumed automation. Behind this rhetoric lies the de-humanization of machine computation, i.e. the removal of all the processual, decisional, ‘oriented’ aspects informing every online recommender system. This essay focuses on content-to-content video recommendations, which are based on patterns of similarity between different contents, and it intends to show that there is nothing neutral — even in the most seemingly ‘objective’ form of video recommendation. The aim is to rediscover those very processual elements of the ‘data supply chain’ — regarding how metadata are created and collected, and how algorithms are con gured — so as to make them critically observable again: the funnels, decision points, the multiple layers of human mediation and ltering, in both their relevance and sensitivity. https://riviste.unimi.it/index.php/cinemaetcie/article/view/16569
spellingShingle Giorgio Avezzù
The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems
Cinéma & Cie
title The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems
title_full The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems
title_fullStr The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems
title_full_unstemmed The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems
title_short The Data Don’t Speak for Themselves: The Humanity of VOD Recommender Systems
title_sort data don t speak for themselves the humanity of vod recommender systems
url https://riviste.unimi.it/index.php/cinemaetcie/article/view/16569
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