Product Recommendation


Personalized products, whether online content recommendations or customized periodic services have become the typical need of every user nowadays. The commodities are now preprogrammed to render user-specific results. Even if the exact user requirements aren’t available, the AI (Artificial Intelligence) based product recommendation systems point to the most relevant product that is still suitable to the user requirements. Such intelligent systems also recommend the user’s products/content/services based on their previous interactions with the system. A product recommendation system is a new way of letting users explore and know what they like the most. Once the system can predict the user’s personalized requisites with high accuracy, the user only makes multiple comebacks to the same system owing to higher satisfaction. So, with AI-based recommendation systems, the number of regular users keeps increasing.

Challenges Faced by the Customers

Filter bubble & Echo-chamber

Filter bubbles & Echo-chamber always limit the product recommendations on the news feed. This limits the bright prospects of users to reach the farthest corner of endless possibilities available for their products. Though the elements above are one of the consequences of product recommendation, modern recommendation systems focus on creating and developing personalized products for users. This way, the users keep getting the product with new and personalized features.

Misleading recommendations

Users must manually filter out irrelevant products/services/content results daily. This is a tedious task as the user must revalidate the authenticity of every irrelevant material encountered daily. The product recommendation systems play a vital role by producing user-specific suggestions with good testimonials.

In a nutshell

Recommendations of irrelevant, repetitive, and misleading products can annoy users. The AI-based recommendation systems check for a proper monitoring system whereby it evaluates the user’s interaction with the plan to assess and filter out unnecessary content.
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