{"type":"ScholarlyArticle","title":"MPMA: Mixture Probabilistic Matrix Approximation for Collaborative Filtering","authors":[{"name":"Chao Chen","slug":"chao-chen","given":"Chao","family":"Chen","affiliation":""},{"name":"Dongsheng Li","slug":"dongsheng-li","given":"Dongsheng","family":"Li","affiliation":""},{"name":"Qin Lv","slug":"qin-lv","given":"Qin","family":"Lv","affiliation":""},{"name":"Junchi Yan","slug":"junchi-yan","given":"Junchi","family":"Yan","affiliation":""},{"name":"Stephen M. Chu","slug":"stephen-m-chu","given":"Stephen M.","family":"Chu","affiliation":""},{"name":"Li Shang","slug":"li-shang","given":"Li","family":"Shang","affiliation":""}],"abstract":"Matrix approximation (MA) is one of the most popular techniques for collaborative filtering (CF). Most existing MA methods train user/item latent factors based on a user-item rating matrix and then use the global latent factors to model all users/items. However, globally optimized latent factors may not reflect the unique interests shared among only subsets of users/items, without which unique interests of users may not be accurately modelled. As a result, existing MA methods, which cannot capture the uniqueness of different user/item, cannot provide optimal recommendation. In this paper, a mixture probabilistic matrix approximation (MPMA) method is proposed, which unifies globally optimized user/item feature vectors (on the entire rating matrix) and locally optimized user/item feature vectors (on subsets of user/item ratings) to improve recommendation accuracy. More specifically, in MPMA, a method is developed to find both globally and locally optimized user/item feature vectors. Then, a Gaussian mixture model is adopted to combine global predictions and local predictions to produce accurate rating predictions. Experimental study using MovieLens and Netflix datasets demonstrates that MPMA outperforms five state-of-the-art MA based CF methods in recommendation accuracy with good scalability.","doi":null,"url":"https://ijcai.org/proceedings/2016/199","pdf":"https://ijcai.org/proceedings/2016/0199.pdf","markdown":"https://ijcai.org/proceedings/2016/199.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/199","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":199,"pages":{"first":1382,"last":1387},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}