{"id":638,"date":"2022-03-02T13:34:57","date_gmt":"2022-03-02T18:34:57","guid":{"rendered":"https:\/\/courses.ideate.cmu.edu\/62-830\/s2022\/?p=638"},"modified":"2022-03-02T16:19:57","modified_gmt":"2022-03-02T21:19:57","slug":"recommendation-algorithms-are-pervasive-now-they-need-to-diversify","status":"publish","type":"post","link":"https:\/\/courses.ideate.cmu.edu\/62-830\/s2022\/?p=638","title":{"rendered":"Recommendation Algorithms are Pervasive. Now They Need to Diversify."},"content":{"rendered":"\n
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Unsplash<\/em><\/figcaption><\/figure>\n\n\n\n

Artificial intelligence in the arts is growing increasingly more complex. It’s 2022, and robots are co-creating art<\/a>, NFTs are celebrating and confusing art consumers around the world, and at least 85%<\/a> of Americans have smartphones that give immediate access to endless amounts of streaming content. This article specifically covers the recommendation algorithms built to support content delivery for this majority, their cultural implications, and bias control.<\/p>\n\n\n\n

Pervasive Algorithms<\/h4>\n\n\n\n


New technologies in the crypto realm and metaverse have distracted many from the increasing prevalence of social media and content streaming applications like Spotify, Facebook, YouTube, TikTok, Twitter, etc. So, before delving into the unsolved problems with these services, it is worth reviewing how important they are in our lives through examining consumer data. According to the Pew Research Center, a majority of adults use social media applications at least once a day. The algorithms these applications use to present relevant content to their users vary technically across companies, but one element remains the same between them. The algorithms are constantly improving their modeling capabilities using a combination of content-based, collaboration-based, and knowledge-based methods<\/a>. Content-based methods use metadata about items presented to recommend what a user would like to see, whereas collaborative and knowledge-based methods use user profiles and behavior and a combination of user and content data, respectively, to share content relevant to specific users. The high-level goal of the algorithm, regardless of its specific structure, is to add personalization and enjoyment to the application for its user.<\/p>\n\n\n\n

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Pew Research Center \u201cSocial Media Use in 2021\u201d<\/figcaption><\/figure>\n\n\n\n

The Center for Humane Technology calls the cause of our daily reliance on personalized social media and content streaming applications \u201cpervasive technology<\/a>.\u201d Advanced recommendation algorithms disrupt our daily lives because they constantly reinvent what we see to appeal to our psychological wiring. For example, recommending dramatic or violent social media content appeals to human curiosity, and creating endless scrolling in an application gives users the power to continue engaging in that curiosity consumption without bound. When asked to describe positive experiences with recommendation algorithms, a group of peers responded that they enjoy receiving a mixture of similar and new content, events to attend based on past consumption, and niche accounts that make them feel \u201cextra special and unique.\u201d The common thread in these sentiments is the feeling of being known by the application. Recommendation algorithm tuning has progressed to the point that individuals expect algorithms to understand them, and they reward these systems with their undivided attention when they do.<\/p>\n\n\n\n

TikTok currently has one of the best content recommendation algorithms on the market in terms of capturing users\u2019 interests and recommending desired content. It is estimated that 90%-95% of what TikTok users watch on the application comes from the TikTok algorithms\u2019 recommendations. For reference, only approximately 70%<\/a> of YouTube views can be traced to its recommendation algorithms, and YouTube is widely considered one of the best content streaming platforms with more than 2 billion active users. TikTok is still changing its algorithm often to make even more improvements in content recommendation, but this video provides a general use case for exploring how it curates individualized content.<\/p>\n\n\n\n

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