Name: Collaborative Filtering - Collaborative filtering
Uploaded: 2020-12-18T00:00:00Z
Description: Discover new items to recommend to users by finding others with similar tastes. Learn to make user-based and item-based recommendations—and in what context they should be used. Use k-nearest neighbors models to leverage the wisdom of the crowd and predict how someone might rate an item they haven’t yet encountered.