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AI Recommendation Systems

AI recommendation systems are types of Machine learning algorithm that provides suitable information to consumers based on their attributes or previous behaviour in the system. Banking, Insurance, Financial Services, more lately Trading applications and platforms have all embraced these solutions.

Companies like Geico and Progressive which learn and recommend  product based on past search history, or Amazon, which proposes things based on your browsing, buy history, or relation to intended purchases, are popular examples of recommender systems.

AI Recommendation Systems

Recommendation systems are useful in a variety of fields, including:

  • E-commerce and Retail
  • Content recommendations for entertainment subscription services
  • Hyper-personalization of products and services in the travel industry
  • Websites for news and social media
  • Banking and financial services for new products
A recommender system may be the solution if you want to increase user engagement, basket size, share of wallet, or ensure continued relevance to your customers.
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Advantages of an Automated Recommendation System
Automated Recommendation System
How to Set Up a Recommendation System

Here are a few of the most crucial things to think about

Data Gathering

Data Gathering

User activity is used to collect Data (e.g. browsing history, past orders).

Pre-processing of data

Pre-processing of data

The Data is converted into a format that may be used to train a model. Data annotation, cleansing, and organizing are examples of pre-processing.

Training of Model

Training of Model

Data that has been curated is used to train a model that learns a set of pattern rules that can be applied to fresh data.

data output

Output

The program may provide personalized suggestions based on a user's prior behavior - or users who have common qualities or behaviors.