Despite this, the SEO industry tends to focus on sayings like “YouTube is the world’s second largest search engine,” and emphasize ranking in YouTube search results or getting YouTube listings in Google search results.
Yet this paper is rarely referenced by the SEO industry.
While YouTube is now able to create automated closed captions for videos and its capacity to extract information from video has improved dramatically over the years, you should not rely upon these if you want YouTube to recommend your video.
YouTube’s paper on the recommendation algorithm mentions that metadata is an important source of information, although the fact that metadata is often incomplete or even entirely missing is an obstacle that their recommendation engine is designed to overcome as well.
To avoid forcing the recommendation engine to do too much work, make sure that every metadata field is populated with the right information with every video you upload:
Include your target keyword in the video title, but make sure the title also grabs attention and incites curiosity from users.
Include a full description that uses your keyword or some variation on it, and make sure it is at least 250 words long.
Include the major points you will cover in the video and the primary questions that you will address.
Include your primary keyword and any variations, related topics that come up in the video, and other YouTubers you mention within the video.
If your playlists do well, then your video can become associated with keeping users on YouTube longer, leading to your video showing up in recommendations.
Use an eye-catching thumbnail. Good thumbnails typically include some text to indicate the subject matter and an eye-catching image that creates an immediate emotional reaction.
While YouTube’s automated closed captions are reasonably accurate, they still often feature misinterpretations of your words. Whenever possible, provide a full transcript within your metadata.
Use your keyword in your filename. This likely doesn’t have as much impact as it once did, but it certainly doesn’t hurt anything.
2. Video Data
The data within the video itself is becoming more important every day.
Including videos or images within your videos referencing your keywords and related topics will likely help improve your video’s relevancy scores in the future, assuming these technologies aren’t already in motion.
3. User Data
- Explicit: This includes liking videos and subscribing to video channels.
- Implicit: This includes watch time, which the paper acknowledges doesn’t necessarily imply that the user was satisfied with the video.
4. Understanding Co-Visitation
Importantly, similar videos are here defined as videos that the user is more likely to watch (and presumably enjoy) after seeing the initial video, rather than necessarily having anything to do with the content of the videos being all that similar.
This mapping is accomplished using a technique called co-visitation.
The co-visitation count is simply the number of times any two videos were both watched within a given time period, for example, 24 hours.
To determine how related two videos are, the co-visitation count is then divided by a normalization function, such as the popularity of the candidate video.
In other words, if two videos have a high co-visitation count, but the candidate video is relatively unpopular, the relatedness score for the candidate video is considered high.
In practice, the relatedness score needs to be adjusted by factoring in how the recommendation engine itself biases co-visitation, watch time, video metadata, and so on.
Practically speaking, what this means for us is that if you want your video to pick up traffic from recommendations, you need people who watched another video to also watch your video within a short period of time.
There are a number of ways to accomplish this:
- Creating response videos within a short time after an initial video is created.
- Publishing videos on platforms that also sent traffic to another popular video.
- Targeting keywords related to a specific video (as opposed to a broader subject matter).
- Creating videos that target a specific YouTuber.
- Encouraging your viewers to watch your other videos.
5. Factoring In-User Personalization
YouTube’s recommendation engine doesn’t simply suggest videos with a high relatedness score. The recommendations are personalized for each user, and how this is done is discussed explicitly within the paper.
For the simplest recommendation engine, the videos with the highest relatedness score would then simply be selected and included in the recommendations.
However, YouTube discovered that these recommendations were simply too narrow. The recommendations were so similar that the user would likely have found them anyway.
In other words, to show up as a suggested video, you don’t necessarily need to have a high co-visitation count with the video in question. You could make do by having a high co-visitation count with a video that in-turn has a high co-visitation count with the video in question.
For this to ultimately work, however, your video will also need to rank high in the recommendations, as discussed in the next section.
6. Rankings: Video Quality, User Specificity & Diversification
YouTube’s recommendation engine doesn’t simply rank videos by which videos have the highest relatedness score. Being within the top N relatedness scores is simply pass/fail. The rankings are determined using other factors.
The YouTube paper describes these factors as video quality, user specificity, and diversification.
Quality signals include:
- User ratings.
- Upload time.
- View count.
The paper doesn’t mention it, but session time has since become the driving factor here, in which videos that lead to the user spending more time on YouTube (not necessarily on that YouTube video or channel) rank better.
These signals boost videos that are a good match based on the user’s history. This is essentially a relatedness score based on the user’s history, rather than just the seed video in question.
Videos that are too similar are removed from the rankings so that users are presented with a more meaningful selection of options.
This is accomplished by limiting the number of recommendations using any particular seed video to select candidates, or by limiting the number of recommendations from a specific channel.
The YouTube recommendation engine is central to how users engage with the platform.
Understand how YouTube works will dramatically improve your chances of doing well on the world’s most popular video site.
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