Automated Recommendation System​

A recommender system is a machine learning method that suggests relevant content to users based on their characteristics or historical behaviour within the system. These solutions have become increasingly popular among e-commerce, advertising, entertainment and, more recently, training apps and platforms. ​

Popular examples of recommender systems include apps such as Spotify and Netflix, where the system learns and recommends songs or movies based on the probability of the user liking the content, or Amazon, which suggests products based on your browsing, purchase history or relevance to intended purchases.

Recommendation systems are powerful applications in industries such as:

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Retail and e-commerce​​

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Entertainment subscription services – content recommendation​

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Travel sector and hyper-personalization of products and services​​

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News websites and social networks​

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Banking and financial services for new products​​​


If you want to increase user engagement, increase basket size, grow share of wallet or ensure on-going relevance to your customers, then recommender system may be a solution for you.​

Benefits of Automated Recommendation System​

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How to Build a Recommendation System​

Here are some of the key elements to consider​​
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