#MILLION SONG CHALLENGE ALTERNATING LEAST SQUARES MOVIE#
The Netflix Prize sought to substantially improve the accuracy of predictions about how much someone is going to enjoy a movie based on their movie preferences (ratings).Ī very successful approach to this problem are the so called latent factor models using Matrix Factorization. The Nextflix competition brought about a sea change in the way CF engines analyze and compute predictions. Some researchers have combined ARM and CF to provide personalized recommendations. But this technique does not generate personalized recommendations. The following images demonstrate how Mahout's recommendation engine work:Īnother approach is to use association rule mining (or market basket analysis) to compute interesting recommendations. As of v0.7, Mahout provides the standalone version of the original item-item, user-user and slope-one recommender as well as the distributed versions of item-item CF. Later, Taste was incorporated into Apache Mahout. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself.įirst, there was 'Taste', a simple collaborative filtering engine that could predict what a user would like next - be it a movie, book, or a product.
page views, clicks, etc.Ĭollaborative filtering (CF) methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities, or preferences - and predicting what users will like based on their similarity to other users. movies, music while others do well with implicit ratings e.g.
people or groups) they had not yet considered. Some techniques work well with explicit ratings e.g. Recommendation systems seek to predict the 'rating' or 'preference' that a user would give to an item (such as music, books, or movies) or social element (e.g.