How YouTube’s recommendation system works? – Burning Issues – Free PDF Download

Youtube Algorithm

• How does YouTube determine which video to recommend for consumption?
• How exactly does it decide which videos end up on one’s YouTube homepage?

• whether sensationalist and misleading content or what the company calls ‘borderline content’ gets more views on the platform.
• The post also tries to answer how YouTube has been trying to ensure that it does not end up recommending such content.

•  recommendations “drive a significant amount of the overall viewership on YouTube,” and that this is higher than “even channel subscriptions or search.”
• He also notes that YouTube wants to limit “views of borderline content from recommendations to below 0.5% of overall views” on the platform.
• borderline” content — videos that toe the line between what’s acceptable and what violates YouTube’s terms of service

• YouTube’s recommendation system works in two main places.
• One is the YouTube homepage, which usually has a mix of content from subscribed channels to recommended videos, which the platform thinks you are likely to see.
• Recommendations also work in the ‘Up Next’ panel, when you are done watching a video, and YouTube lines up a second video that it thinks you are likely to watch.

• The post explains that YouTube’s recommendations systems “don’t operate off of a ‘recipe book’ of what to do,” but is constantly evolving and rely on certain signals.
• The signals range from clicks, watchtime, survey responses, and actions around videos such as sharing, clicking on the like or dislike button.

• Clicks: If one clicks on a video, this is seen as a strong indicator that one will watch the video. But the blog post notes that over the years YouTube has realised that just because one clicks a video, doesn’t mean it is high up on their preference list.
• After all, deceptive and click-bait video thumbnails are used to lure viewers in, who then realise that the video isn’t something they prefer.
• Watchtime: This looks at videos which one has watched and for how long, in order to provide “personalised signals” to YouTube’s systems.
• For instance, if one is a fan of comedy content on the platform, and spends hours watching, then it is likely to be all over the recommendations. It is a safe bet that the user will watch such comedy videos.

• But not “all watchtime is equal,” which is why they also consider other signals when deciding recommendations.
• Survey Responses: YouTube says this is done to measure “valued watchtime”—the time spent watching a video that a user considers valuable.
• Surveys include asking users to rate videos out of five stars, and if a user marks a video as low or high, they usually have follow-up questions.
•  Only videos rated highly with four or five stars are counted as valued watchtime. Responses from these surveys have been used to train “a machine learning model to predict potential survey responses for everyone,” by YouTube.

• Sharing, Likes, Dislikes: Likes, shares, dislikes on a video are also considered. The assumption is that if one enjoyed a video they will press the like button or might even share it.
• This information is further used to “try to predict the likelihood that you will share or like further videos.” Dislike obviously is a strong indicator that the video didn’t appeal to the user.

• also explains that the importance allotted to each signal depends on the user. “If you’re the kind of person to share any video that you watch, including the ones that you rate one or two stars, our system will know not to heavily factor in your shares when recommending content,”
• YouTube says that the recommendation system doesn’t have a “fixed formula” but “develops dynamically” and keeps up even up with changes in viewing habits.
• YouTube like all other social media platforms such as Facebook, Twitter, is facing criticism that it does not do enough to curtail the spread of misinformation.

• And this is the context behind why YouTube is opening up about how its recommendation system works.
• However, the company says that they don’t want to recommend low-quality content or ‘borderline’ content, which is problematic but doesn’t violate its rules outright.
•  Examples include videos claiming the Earth is flat or those that claim to offer a cure for cancer with ‘natural remedies.’
• YouTube states it has limited recommending low-quality content since 2011 when it built “classifiers to identify videos that were racy or violent and prevented them from being recommended.”
• Further in 2015, it started demoting videos with “sensationalistic tabloid content that was appearing on homepages.”
• Evaluators try and answer some questions around the video: Whether it has expertise, the reputation of the channel, the speaker, etc. “The higher the score, the more the video is promoted when it comes to news and information content,”
• Videos are also accessed for whether the content is misleading, inaccurate, deceptive, hateful, or with the potential to cause harm.
•  Based on all of these factors a video gets a score; the higher the score, the more YouTube’s recommendation system will promote it. A lower score means the video is classified as borderline and is demoted in recommendations.
• The post admits that the company knows it is leaving up “controversial or even offensive content,” at times, but adds that “it wants to focus on building a recommendation system that doesn’t promote this content.”
• YouTube admits the problem is far from being solved but states that “it will continue refining and investing in its system to keep improving it.”

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