“Books/Movies/Shows like XYZ”. How many times have you Googled the above phrase to get suggestions on your next entertainment escapade? Probably often. There is a wealth of content available online, and no matter how many filters you apply, it is impossible to go through them all to find what suits your preferences. So naturally, we turn to friends, colleagues, or even technology for content recommendations on media networks.

They know what we have liked so far, so they can tell what we might like next!

Content based recommendation filtering


What are Recommender Systems?

A  recommendation system, or a recommendation engine, is an algorithm based on cutting-edge technology such as Artificial Intelligence (particularly Machine Learning), which utilizes Big Data and customer behavior to make relevant and customized recommendations.

These suggestions could be based on factors like past viewership/readership, demographic information, search history, affinity topics, recent interests, and many.

Typically, recommender systems operate on the following models:

  • Collaborative Filtering
  • Content-based Filtering
  • Demographic-based Filtering
  • Utility-based Filtering
  • Knowledge-based Filtering
  • Hybrid Filtering

Why Should Media Websites Invest in Content Recommender Systems?

On the surface, the role of content-based recommendation systems is obvious: to narrow down the selection pool and enhance the digital customer experience. Naturally, tailored content would birth better engagement (think of all the hours one spends binge-watching Netflix!)

However, Content recommendations on media websites add more value  in the following ways:

  • Better Retention: As consumers get to explore content that interests them, they are more likely to explore your collection rather than opt for your competitor.
  • Enhanced Engagement: As the media website continues to make relevant recommendations, it becomes easier to target them with fresh or recirculated content.
  • Build Loyalty: Customers appreciate businesses that value their time. Through relevant content-based recommendations, you save them from a long-drawn search process and cut right to what truly matters – content consumption.
  • Increase Sales: For media websites that run subscription services, content recommender systems can serve trigger cues that facilitate sales and upselling.
  • Scalability: Content recommender systems operate on customer data and match it up with automated metadata generation, making it easier to scale up or down depending on the number of users.
  • Recycle Content: Content-based recommendations on Media websites, not only add to the digital experience but also capture highly specific interests of the user, and recommend niche content that very few other users may have an interest in.

How Can Media Websites Deliver Content Recommendations?

So, how do we integrate content recommender systems on your media websites? Here are a few use cases to get you started:

Suggest the Next Article or Blog Post

An example how CNN uses content based recommendation

The struggle is real – a user visits your website, consumes a specific content, and leaves never to come back! Encourage them to read your other posts by suggesting highly-relevant blog posts and articles to each visitor.

Suggest Similar Content Assets Across the Website

Media websites that are focused on demand generation can offer other types of relevant content such as case studies, videos, and white papers to their visitors. But who goes around looking for these, right? A suitable strategy, in this case, would be to recommend this content as additional resources for their area of interest.

Make Search More Efficient

On several occasions, users who make use of search bars are looking for specific content. Obviously, they would not enjoy sifting through tons of content to locate what they seek. Websites, to aid these users in this quest, can suggest the most relevant content for every individual as they search. A content recommendation on a media website is proven to be valuable for efficiency in user engagement.

Allow Users to Bounce Back to Previously-Viewed Content

Adding a feature along the lines of a “Recently Viewed” tab can make it easier and more efficient for the visitors to locate the content that they may have engaged with recently. As a result, they can effortlessly pick up from where they left off.

Incorporate Dynamic CTAs

Dynamic content suggestion on CNN

Your media website may promote certain content on the homepage or any other prominent location on the landing page. But what happens after the user has already consumed that content? You wouldn’t want to miss out on an opportunity to engage them with something else and waste it on continuing to promote the stale content! Through content recommendations on the media network system, you can showcase new and meaningful content that will guide them down your website’s rabbit hole!

Final Thoughts on the Content recommendations On Media website

Content-based recommendations on media network systems offer a unique opportunity to add value to digital customer experience and user satisfaction. However, companies must also add creativity to their algorithms to help users discover other aspects of your website.

So go ahead, and get recommending!


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