Abstract: Recommendation control techniques using incremental matrix factorization and clustering are described. User latent factors and item latent factors are computed from data that denotes ratings associated with the users regarding respective ones of the plurality of items of digital content. Data is obtained that describes interaction of a particular one of the users with at least one respective item of the digital content. A plurality of clusters is formed using the user latent factors. The recommendations are generated using the user latent factors and the item latent factors for each of the plurality of clusters. Further, at least one of recommendations is located based on comparison of a user identifier of a subsequent user with the plurality of clusters. Interaction of the subsequent user with the digital content is controlled based on the located at least one of the recommendations.