{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:34:47Z","timestamp":1723016087783},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matrix factorization, which predicts unobserved entries as linear combinations of latent variables. We generalize to non-linear combinations in massive-scale matrices. Bayesian approaches have been proven beneficial in linear matrix completion, but not applied in the more general non-linear case, due to limited scalability. We introduce a Bayesian non-linear matrix completion algorithm, which is based on a recent Bayesian formulation of Gaussian process latent variable models. To solve the challenges regarding scalability and computation, we propose a data-parallel distributed computational approach with a restricted communication scheme. We evaluate our method on challenging out-of-matrix prediction tasks using both simulated and real-world data.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/454","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"3275-3281","source":"Crossref","is-referenced-by-count":2,"title":["Scalable Bayesian Non-linear Matrix Completion"],"prefix":"10.24963","author":[{"given":"Xiangju","family":"Qin","sequence":"first","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT"},{"name":"Aalto University, 00076 Espoo, Finland"}]},{"given":"Paul","family":"Blomstedt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT"},{"name":"Aalto University, 00076 Espoo, Finland"}]},{"given":"Samuel","family":"Kaski","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Helsinki Institute for Information Technology HIIT"},{"name":"Aalto University, 00076 Espoo, Finland"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:49:24Z","timestamp":1564285764000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/454"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/454","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}