{"type":"ScholarlyArticle","title":"Practical Linear Models for Large-Scale One-Class Collaborative Filtering","authors":[{"name":"Suvash Sedhain","slug":"suvash-sedhain","given":"Suvash","family":"Sedhain","affiliation":""},{"name":"Hung Hai Bui","slug":"hung-hai-bui","given":"Hung Hai","family":"Bui","affiliation":""},{"name":"Jaya Kawale","slug":"jaya-kawale","given":"Jaya","family":"Kawale","affiliation":""},{"name":"Nikos Vlassis","slug":"nikos-vlassis","given":"Nikos","family":"Vlassis","affiliation":""},{"name":"Branislav Kveton","slug":"branislav-kveton","given":"Branislav","family":"Kveton","affiliation":""},{"name":"Aditya Krishna Menon","slug":"aditya-krishna-menon","given":"Aditya Krishna","family":"Menon","affiliation":""},{"name":"Trung Bui","slug":"trung-bui","given":"Trung","family":"Bui","affiliation":""},{"name":"Scott Sanner","slug":"scott-sanner","given":"Scott","family":"Sanner","affiliation":""}],"abstract":"Collaborative filtering has emerged as the de facto approach to personalized recommendation problems. However, a scenario that has proven difficult in practice is the one-class collaborative filtering case (OC-CF), where one has examples of items that a user prefers, but no examples of items they do not prefer. In such cases, it is desirable to have recommendation algorithms that are personalized, learning-based, and highly scalable. Existing linear recommenders for OC-CF achieve good performance in benchmarking tasks, but they involve solving a large number of a regression subproblems, limiting their applicability to large-scale problems. We show that it is possible to scale up linear recommenders to big data by learning an OC-CF model in a randomized low-dimensional embedding of the user-item interaction matrix. Our algorithm, Linear-FLow, achieves state-of-the-art performance in a comprehensive set of experiments on standard benchmarks as well as real data.","doi":null,"url":"https://ijcai.org/proceedings/2016/542","pdf":"https://ijcai.org/proceedings/2016/0542.pdf","markdown":"https://ijcai.org/proceedings/2016/542.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/542","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":542,"pages":{"first":3854,"last":3859},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}