As you flip through Netflix on a lazy Sunday afternoon, consider the options at your disposal: comedies, thrillers, romances, horrors. You find yourself drawn to a particular selection, but why? Is it your genuine preference, or the result of cunning programming? The answer is a little bit of both. Welcome to the era of algorithmically curated entertainment, a time where your viewing choices, reading lists, and even your music playlist are personalized to match your preferences.
Algorithms, mathematical formulas used to perform complex tasks and calculations, were first introduced into entertainment to improve the search process. Consumers have an immense variety of content to choose from. Consider, for example, Netflix’s repository of 13,500+ titles, Spotify’s library of 50+ million songs, or Amazon’s selection of 12 million products. Humans navigating through them manually would be tedious and inefficient. But algorithms make it manageable, and even enjoyable.
The application of algorithms in entertainment has evolved beyond mere search facilitation to personalized curation. Companies utilize a machine learning approach termed “recommendation systems.” These systems analyze your behavior, compare it with others’ and suggest content you may like.
Netflix is a case in point. Their recommendation algorithm deciphers viewerships patterns. What show did you binge-watch in one night? Did you pause, skip or re-watch any scene? What were you watching when you invited friends over? The algorithm knows. It uses these clues to tailor your virtual library.
Amazon, the retail giant, is no different. Based on your shopping history, the product you view, the pages you spend the most time on, and even the products you search for, the Amazon recommendation system suggests what you might like next.
This advanced personalization does present potential ethical concerns, however.
For instance, it may lead to what Eli Pariser termed as “filter bubbles.” Pariser explained this in his book “The Filter Bubble: What The Internet Is Hiding From You”, whereby the algorithmic curation limits exposure to content conflicting with user’s beliefs, eventually isolating them into their ideological spaces.
Moreover, there’s the question of privacy. To provide personalized recommendations, the platforms have to track user behavior intricately. While most platforms state they anonymize the data and use it only to improve the user experience, privacy concerns persist.
Although algorithms have reshaped entertainment and personalized our experiences, they are not fully infallible. Algorithms are not completely immune to biases. They’re only as impartial as the data they’re provided. If the input data is biased, the recommendation will mirror that.
Algorithmic accuracy also becomes questionable when it comes to catering to dynamic human behaviors. Cinema is a subjective medium and what may enthrall one might completely bore another. Considering this, can an algorithm truly capture the complexities of human preferences? Perhaps not completely, but they are undoubtedly coming close.
As the saying goes, “Algorithms rule the world.” But would it be an overstatement to say that they rule our entertainment world? Unarguably, these invisible silent workers behind our screens are working tirelessly to ensure an enjoyable, seamless, and personalized user experience.
Despite the concerns and imperfections, algorithmic curation is here to stay, and with advancements in artificial intelligence and machine learning, they’re bound to get more astute at crafting our personal entertainment universes.
SOURCES:
Eli Pariser. “The Filter Bubble: What The Internet Is Hiding From You”. Penguin UK, 2011.
Netflix Media Center. “Netflix Now: Gripping Series, Provoking Documentaries,
Tempting Gastronomies”. www.media.netflix.com.
Amazon Company Statistics. “How Much Does Amazon Make?”. www.sellbrite.com.
Spotify Official website. “Facts and Figures”. www.newsroom.spotify.com.
‘Algorithmic Bias and Fairness: An overview’, Sarah Roberts, UCLA Center for Critical Internet Inquiry. 2020.
‘Big Data, Little Privacy: How the digital age is destroying our privacy’, Rob Kitchin, Routledge, 2020.
