{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accurate-and-scalable-social-recommendation","title":"Accurate and scalable social recommendation using mixed-membership stochastic block models","arxiv_id":"1604.01170","date":"2016-04-05","proceeding":null,"authors":["Antonia Godoy-Lorite","Roger Guimera","Cristopher Moore","Marta Sales-Pardo"],"abstract":"With ever-increasing amounts of online information available, modeling and\npredicting individual preferences-for books or articles, for example-is\nbecoming more and more important. Good predictions enable us to improve advice\nto users, and obtain a better understanding of the socio-psychological\nprocesses that determine those preferences. We have developed a collaborative\nfiltering model, with an associated scalable algorithm, that makes accurate\npredictions of individuals' preferences. Our approach is based on the explicit\nassumption that there are groups of individuals and of items, and that the\npreferences of an individual for an item are determined only by their group\nmemberships. Importantly, we allow each individual and each item to belong\nsimultaneously to mixtures of different groups and, unlike many popular\napproaches, such as matrix factorization, we do not assume implicitly or\nexplicitly that individuals in each group prefer items in a single group of\nitems. The resulting overlapping groups and the predicted preferences can be\ninferred with a expectation-maximization algorithm whose running time scales\nlinearly (per iteration). Our approach enables us to predict individual\npreferences in large datasets, and is considerably more accurate than the\ncurrent algorithms for such large datasets.","url_abs":"http://arxiv.org/abs/1604.01170v2","url_pdf":"http://arxiv.org/pdf/1604.01170v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"accurate-and-scalable-social-recommendation","repo_url":"https://github.com/billjeffries/mixMemRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}