Accelerating model-based collaborative filtering with item clustering
Robin Devooght, Hugues Bersini · 2018
We present a clustering method designed to complement existing model-based collaborative filtering algorithms. Thanks to clustering, we are able to produce faster recommendations on datasets with a very large number of items. Moreover, we can work with models independent of the number of users, and produce recommendations for new users and in session based environment. We show that our method builds meaningful clusters, and that we can reduce the number of items considered during recommendation by a factor up to 50, with little impact on the accuracy or diversity of the recommendation.