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Collaborative filtering

What is Collaborative Filtering?

Collaborativefiltering is a technique that is often used to create personalized recommendations on the web. Some popular websites that use collaborative filtering technology include Amazon, Netflix, iTunes, and IMDB. Collaborative filtering uses algorithms to make automatic predictions about a customer’s interests by combining preferences from multiple users.

For example, a website such as Amazon may recommend that customers who buy books A and B should also buy book C. This is done by comparing the historical preferences of those who bought the same books.

How does it work?

There are the following different types of collaborative filtering:
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  • Memory-based
  • : The memory-based filtering method uses user rating information to calculate the similar preferences between users or customers. These calculations are then used to make purchase recommendations.

  • Model-based
  • : With the help of data mining, models are created. The system also learns algorithms to look for buying habits by using data. The models created are then used to make predictions for real customers.

  • Hybrid method
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: Various programs combine the model-based and memory-based CF algorithms to achieve an ideal result.

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