RECOMENDATION ENGINES

WE HAVE THREE
MAIN APPROACHES:

OFFERING YOUR CUSTOMERS WHAT THEY REALLY WANT IS WHAT YOU NEED

To create our recommendation engines, we use the most common machine learning techniques nowadays, together with the experience of our statisticians.

The objective is to try to infer the tastes and preferences of the users by identifying the unknown interest elements for them.

The biggest challenge of the recommendation system is to find the right method to automatically predict how a product will like a user and / or user groups based on the following facts and data:

Pleasures – Preferences – Habits – Comments – Etc..

Collaborative filtering

They base their logic on the user’s characteristics. In this case, the system will have sufficient user information, preferences, pages that you visit, etc. as well as other users who look like him and have made similar decisions. The items that have been successful with similar users will surely also interest the new user.

Based on content

The feature of these recommenders is that they have the content of the item as the basis of the prediction, instead of the user, that is, it is based on the characteristics of the product/content (category, etc.) in order to do the recommendations.

Hybrid systems

They combine both approaches, colaborative filtering and recommenders based on content.

Generation of the initial model

Working with the customer data to generate the recommendation algorithm.

Model optimization

Correct assignment of variables.

Generation of complex models

Inclusion of exogenous variables and feedback from users.

Model feedback

The results obtained are reintroduced again in
the model to optimize the recommender.