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For the last decade, personalization of content has been associated with filter bubbles. Initiated by Eli Pariser’s theory in his book “The Filter Bubble: What The Internet Is Hiding From You”, the assumption that algorithms would create an environment in which you only encounter familiar opinions or information, has spread like wildfire. Only a few studies have profoundly analyzed the impact of relying on algorithmic personalization for surfacing content on online newspapers.
Based on our experience in news personalization for various digital newspapers around the world, we put forward several avenues which tend to demonstrate that personalization is more of a solution than a problem in relation to this phenomenon:
News websites, apps, and other digital channels are frequently made up of “blocks” of content, serving different purposes (breaking news, highlights from a specific section, recent news, etc.).
Therefore, it is very common to only apply algorithmic personalization to specific blocks.
This personalization engine can power each block with a different treatment, i.e., focusing on distinct objectives. This modularity is key to ensuring a diverse distribution of content and therefore deconstructing filter bubbles by design. This is unlike platforms like Facebook, LinkedIn, or Google that rely solely on one content feed.
Deciding which content to present on the different pages of your website can become a very tedious task given the importance of keeping that content fresh and up to date within a small window of time. When relying on a manual selection of articles, it takes time to select the right content and input this into the system. Personalization, on the other hand, automates this task in real-time and makes it possible to update the content selection in milliseconds for every individual user visiting your website. As a result, it has been proven that personalization can recommend significantly more articles than a human (team) can do on a daily basis. A great example comes from De Telegraaf, the leading digital newspaper in the Netherlands, that showed a manual selection recommends around 250 different articles per day while the personalization solution shows more than 600 different articles (see figure below).
Personalization also does a great job of balancing articles from different sections. Manual selection tends to focus predominantly on general news articles while personalization proposes articles from a wide range of categories.
Another less visible advantage of deploying an algorithmic solution for content curation lies in
the organizational process to achieve it. One part of that process inevitably requires the editorial team to define clear rules for the algorithm to follow that determine which articles should be promoted or blacklisted, the required freshness of content for different positions on the websites, the ratio of paid vs free articles, and more. While you can rely on the algorithms to solve most of these questions, it is often best to discuss these parameters as a team before launching the project to ensure a maximal buy-in.
This creates a real opportunity for the team to structurally fight against filter bubbles with clear editorial guidelines.
There is a myth surrounding the process of computing recommendations for a reader: in other words, proposing “relevant” content would mean promoting content similar to what you’ve already read, hence locking you in a limited amount of content. The reality is quite different. Recent algorithms have dramatically improved the quality of recommendations and their ability to take you from one content piece to another. More specifically, the replacement of content-based approaches (looking at the characteristics of content you liked, such as the topic, and recommending more of that) for behavioral approaches (looking at what readers usually read) has played an important role in breaking these filter bubbles. Since then, personalization systems that are anchored in real-time computation of your preferences outperform humans in proposing diverse and balanced content selection that supports readers in their discovery of your content.
What does this mean for you and your personalization strategy? It all comes down to implementing the right strategy in the right way.
Interested in knowing more about personalization? We recommend you to explore other research we did on our website.
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