27. Collaborative Filtering | Machine Learning with Python | Tech2Teach
In this video, we’ll be covering a recommender
system technique called, Collaborative filtering. So let’s get started.
Collaborative filtering is based on the fact that relationships exist between products
and people’s interests. Many recommendation systems use Collaborative
filtering to find these relationships and to give an accurate recommendation of a product
that the user might like or be interested in.
Collaborative filtering has basically two approaches: User-based and Item-based.
User-based collaborative filtering is based on the user’s similarity or neighborhood.
Item-based collaborative filtering is based on similarity among items.
Let’s first look at the intuition behind the “user-based” approach.
In user-based collaborative filtering, we have an active user for whom the recommendation
is aimed. The collaborative filtering engine, first
looks for users who are similar, that is, users who share the active user’s rating
patterns. Collaborative filtering bases this similarity
on things like history, preference, and choices that users make when buying, watching, or
enjoying something. For example, movies that similar users have
rated highly. Then, it uses the ratings from these similar
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