title: Accurate and scalable social recommendation using mixed-membership stochastic block models.
abstract: With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.
authors.affiliation: Departament d'Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain; antonia.godoy@urv.cat.
authors.forename: Antonia
authors.initials: A
authors.lastname: Godoy-Lorite
authors.affiliation: Departament d'Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain.|Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Catalonia, Spain.
authors.forename: Roger
authors.identifier: 0000-0002-3597-4310
authors.initials: R
authors.lastname: Guimerà
authors.affiliation: Santa Fe Institute, Santa Fe, NM 87501.
authors.forename: Cristopher
authors.initials: C
authors.lastname: Moore
authors.affiliation: Departament d'Enginyeria Química, Universitat Rovira i Virgili, 43007 Tarragona, Catalonia, Spain.
authors.forename: Marta
authors.identifier: 0000-0002-8140-6525
authors.initials: M
authors.lastname: Sales-Pardo
keywords: collaborative filtering; recommender systems; scalable algorithm; social recommendation; stochastic block model