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Grow Big with Recommendation System

By: Vignesh Shanmugam - Oct 11, 2019

Today’s bitter truth is, applications of AI has dramatically increased in our day-to-day life right from what song to listen, what to eat, what to purchase, what to watch and even more. But if you see the same thing in a different perspective, everything saves lots of your time just by providing exactly what you need. This beautiful optimization strategy of Artificial Intelligence is nothing but a Recommendation Engine.

Recommendation Engine: 

In simple terms, Recommendation Engine is a system which helps  us by suggesting products and services based on our interest using Machine Learning algorithms.  Recommendation Engine works like intelligent sales person who is well trained in cross and up selling.

How dose it works?

Here, we will briefly explain the three most common methods to built recommendation engine,

1. Content Based Filtering:

It builds user profiles based on what user liked or bought and offers similar products in the future. For example, this can be used in recommending similar types of products, YouTube videos etc.

2. Collaborative filtering:  

It predicts which items in a set of products a particular customer will like based on the preferences of lots of other people. For example, this can be used in recommending similar types of products, Movies etc.

3. Hybrid filtering:

It uses elements of both content and collaborative based filtering and are more accurate in nature when it comes to performance.

Advantages:

1. Using a recommendation engine on your site will boost up your sales in an unimaginable way.

2. Suggesting products in a better way will improve the user experience.

3. Helping customer in a better way will built a good customer Relationship management.

Recommendation system creates a win-win situation between customer and shopkeeper by providing good user experience and by boosting sales respectively.  

Interested in setting Recommendation system for your business? Mail us at info@kratosolutions.com 


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