Value of metadata in search engines and recommendations

With thousands of hours of video content uploaded to the internet on a daily basis, there is no dearth of media and entertainment content. While most viewers know exactly what they want to watch, others depend on search engines and recommendations to find new movies, tv shows, documentaries, and more.

Metadata plays a very valuable role when it comes to ensuring the right kind of titles are suggested to the viewer. The chances of the correct videos showing up largely depend on how well the videos are optimised with metadata. Search engine algorithms heavily rely on meaningful text to make sense of the video and determine which are the best results for the search query.

What does Metadata do?

Netflix, on the other hand, uses advanced machine learning algorithms with a content recommendation system based on metadata that helps customise the viewers’ overall experience. No matter which platform you use for video content viewing, the value of metadata is very high in terms of indexing the content in a better way. It is vital in increasing the number of views and building a discoverable platform through recommendations and searches.

Why is metadata important for videos on search engines?

Search recommendations are the backbone of how the internet works and one of the main reasons new content is found and featured everyday. By using a set of indexing rules, search engines are optimised to rank certain keywords higher than others. Just like content SEO, Search Engine Optimisation also applies to video content in the same way. When writing your metadata with the right, targeted keywords, you make it much easier for your content to be displayed higher in the ranking.

For businesses that are based online such as streaming services, ranking number one on the search engine page as opposed to ranking at number ten can make quite a big difference. If it is just a few videos, searching through them is easy but when the number of videos increases exponentially, search plays a very big role in helping viewers find what they are looking for.

How to find new content through recommendations?
Video viewing platforms such as Netflix have graduated one step further and incorporated a content recommendation system. Moving beyond using search results as the only way to get videos on your platform noticed, such an advanced system uses machine learning algorithms to create personalised recommendations based on what the viewer has watched in the past.

These content recommendation systems use metadata at their core to classify and analyse each video on the website. Tagging them based on the several genres and sub-genres available is the main metadata that helps train the machine learning algorithms. Having such an advanced internal way of content searching also reduces the chances of the viewer leaving the platform and continue browsing instead of looking elsewhere.

Other advanced capabilities that rely on metadata are autoplay functionality and recommended playlists. By taking information about the users based on their previously watched titles, likes, and dislikes, it allows the system to target different viewers with relevant video title recommendations.

The value of metadata in search engines and recommendations is very high as it helps in segmenting the videos in an easy to classify manner. While machine learning capabilities are being used to further enhance the user experience, metadata for video content will always form the core foundation for how the system works.