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Data Sci."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>Matrix factorization (MF) can extract the low-rank features and integrate the information of the data manifold distribution from high-dimensional data, which can consider the nonlinear neighborhood information. Thus, MF has drawn wide attention for low-rank analysis of sparse big data, e.g., Collaborative Filtering (CF) Recommender Systems, Social Networks, and Quality of Service. However, the following two problems exist: (1) huge computational overhead for the construction of the Graph Similarity Matrix (GSM) and (2) huge memory overhead for the intermediate GSM. Therefore, GSM-based MF, e.g., kernel MF, graph regularized MF, and so on, cannot be directly applied to the low-rank analysis of sparse big data on cloud and edge platforms. To solve this intractable problem for sparse big data analysis, we propose Locality Sensitive Hashing (LSH) aggregated MF (LSH-MF), which can solve the following problems: (1) The proposed probabilistic projection strategy of LSH-MF can avoid the construction of the GSM. Furthermore, LSH-MF can satisfy the requirement for the accurate projection of sparse big data. (2) To run LSH-MF for fine-grained parallelization and online learning on GPUs, we also propose CULSH-MF, which works on CUDA parallelization. Experimental results show that CULSH-MF can not only reduce the computational time and memory overhead but also obtain higher accuracy. Compared with deep learning models, CULSH-MF can not only save training time but also achieve the same accuracy performance.<\/jats:p>","DOI":"10.1145\/3497749","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T12:30:29Z","timestamp":1648211429000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Locality Sensitive Hash Aggregated Nonlinear Neighborhood Matrix Factorization for Online Sparse Big Data Analysis"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4735-794X","authenticated-orcid":false,"given":"Zixuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2989-0679","authenticated-orcid":false,"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-7716","authenticated-orcid":false,"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9392-2597","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4228-6735","authenticated-orcid":false,"given":"Lydia","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electric Engineering, Mathematics and Computer Science, Distributed Systems, Delft University of Technology, Delft, Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York, New Paltz, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1979","volume-title":"Proceedings of the ACM Conference on Information and Knowledge Management","author":"Bai Ting","year":"2017","unstructured":"Ting Bai, Ji-Rong Wen, Jun Zhang, and Wayne Xin Zhao. 2017. 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