Machine Learning

Recommendation Systems

Models that predict which products or content a user may prefer.

Recommendation systems learn from user behaviour, item characteristics or similarities between users. Common approaches include collaborative filtering, content-based filtering, matrix factorisation and neural recommendation models.

They power personalised feeds, shopping suggestions, music discovery and video platforms. Strong recommendation systems must balance relevance with diversity, novelty, fairness and the problem of new users or items.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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