Content Recommendation


Personalized content has become the specific 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 intelligent systems also recommend the user's products/content based on their previous interactions with the system. A content recommendation system is a new way of letting users explore and know what they like the most. Once the system predicts the user's personalized content makes multiple visits to the same scenario due to higher satisfaction. So, with AI-based recommendation systems, the monthly number of users keeps increasing.

Challenges Faced by the Customers

Filter bubble & Echo-chamber

Filter bubbles & Echo-chamber are getting restricted content and specific content on the news feed. It limits the elements mentioned earlier and is considered one of the consequences of content recommendation. Modern recommendation systems focus on creating and developing personalized content for users. In this way, the users keep getting confronted with new content along with personalized ones.

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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