Collaborative filtering is used in recommendation systems. It generates recommendations based on ratings of items by users.
The typical algorithm represents users and items in a matrix and records the ratings in the cells of the matrix. The aim is to match a new user’s preferences for items with other existing users’ preferences. The similarity measures used for matching include cosine similarity, Pearson correlation, and probability-based similarity. For the most similar users any items that the new user has not utilised but have been utilised by matching users, will be recommended.
Model-based collaborative filtering trains a predictive model from user-item ratings matrix. Modellers include Bayesian, latent semantic model, and SVD. Typically the training data consists of user-item pairs and the rating.
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