“Collaborative filtering" (also known as "Community-based recommendation" or "User-based recommendation") is a product recommendation technique that is based on the purchase behaviour of similar customers. Instead of analysing the components or attributes of a product, collaborative filtering uses customers' purchase history information to find patterns and similarities in their preferences.

The collaborative filtering process is divided into two stages: the training phase and the prediction phase. During the training phase, a set of customer purchase history data is used to build a personalised recommendation model. The model uses machine learning and data mining techniques to identify patterns in the purchase behaviour of similar customers and build a similarity matrix that reflects the similarity between customers.

During the prediction phase, the model is used to make personalised recommendations to customers based on their past purchase histories. The model analyses the similarity matrix and a customer's past purchases to determine which products may be of interest to them.

Collaborative filtering is widely used in e-commerce applications, video and music streaming, and in recommendation systems in general. The approach is effective because it is based on actual customer behaviour and does not require detailed information about the products themselves.