Recommendation System

Recommendation System is an information filtering system created to predict products that the user may have an interest in. This technology is based on user's

past behavior as well as similar decisions made by other users. With the System you can create personalized offerings with maximal conversion for each user.

 
01 The user views the products
02
 
Data comes from Live Product Base  
Data comes from Data Management Platform  
Results:
Up to 60%
average conversion increase
Up to 30%
increase in number of products viewed at the online store’s web-site

1. Collaborative Filtering

This method is based on assumption that users, that have had similar interests in the past, will prefer similar products in the future as well.

Collaborative filtering uses the data about the user’s behavior and about decisions made by other users to create personalized recommendations.

 
 

2. Content-based filtering

This method is based on a description of the item and a profile of the user’s preference. In other words, these algorithms recommend items that are similar to those that a user liked in the past (or is examining in the

present). Data mining technology establishes relations between products enhancing the efficiency of the Recommendation System.

 
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