recommender-build

Build product, content or next-item recommendations, and choose the method that fits the data: association rules and market basket analysis, item-based or user-based collaborative filtering, content-based similarity, matrix factorisation with ALS, or graph ranking such as PageRank. Use whenever someone wants to recommend products, songs, articles, courses or offers to users; build "customers also bought", "you may also like", "buy again" or "did you forget" features; mine frequent itemsets with support, confidence and lift; turn clicks, views or purchases into implicit ratings; correct for users who rate harshly or generously; handle cold start for new users or new items; or scale item-item similarity for real-time serving. Not for measuring how good an existing recommender is, and not for segmenting customers into groups.