Content Recommendation


Personalized content has become the typical need of every user nowadays. The search engines are now preprogrammed to render user-specific results. Even, if the exact user query results aren’t available, the AI (Artificial Intelligence) based content recommendation systems point to the most relevant results that are still suitable to the user requirements. Such smart systems also recommend the users products/content based on their previous interactions with the system. Content recommendation system is the new way of letting users explore and know what they like the most. Once the system is able to predict the user’s personalized content with accuracy, only then the user makes multiple visits to same system due to higher satisfaction. So, with AI-based recommendation systems, the monthly users keep increasing.

Challenges Faced by the Customers

Filter bubble & Echo-chamber

Filter bubble & Echo-chamber getting limited content and specific content on the news feed. This limits the Though the aforesaid elements considered to be one of the consequence of content recommendation. The modern recommendation systems focus on creating developing personalized content for the users. In this way, the users keep getting confronted with new content along with the personalized one.

Misleading content

Many users have to manually filter out the irrelevant content on daily basis. This is a tedious task as the user has to revalidate the authenticity of every irrelevant content material encountered on daily basis. The content recommendation systems play a vital role here by producing user specific content only with proper testimonials for the displayed personalized content.

In a nutshell

Recommendation of irrelevant, repetitive and misleading content can be annoying for the users. The AI based recommendation systems check in for proper monitoring system whereby it evaluates the interaction of user with the system to assess and filter out the unnecessary content.
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