202511200953 Status: idea Tags: Datascience, Recommender systems

Collaborative Filtering

• Collaborative filtering methods do not use item or user metadata, but try instead to leverage the feedback or activity history of all users in order to predict the rating of a user on a given item by inferring interdependencies between users and items from the observed activities.
• To train a Machine Learning model with this approach we typically try to cluster or factorize the rating matrix rui in order to make predictions on the unobserved pairs (u, i), i.e. where rui = ”?”.

The advantage of this approach is that the whole set of user-item interactions (i.e. the matrix rui) is used, which typically allows to obtain higher accuracy than using Content-Based models. The disadvantage of this approach is that it requires to have a few user interactions before the model can be fitted

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References

• Dit is iets wat we leren voor Datascience. dit was informatie vanuit avans 2-2 datascience 2025-11-18. en daarbij horen deze slides