{"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/algorithmic-acceleration-of-parallel-als-for","title":"Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark","arxiv_id":"1508.03110","date":"2016-01-10","proceeding":null,"authors":["Winlaw Manda","Hynes Michael B.","Caterini Anthony","De Sterck Hans"],"abstract":"Collaborative filtering algorithms are important building blocks in many\npractical recommendation systems. For example, many large-scale data processing\nenvironments include collaborative filtering models for which the Alternating\nLeast Squares (ALS) algorithm is used to compute latent factor matrix\ndecompositions. In this paper, we propose an approach to accelerate the\nconvergence of parallel ALS-based optimization methods for collaborative\nfiltering using a nonlinear conjugate gradient (NCG) wrapper around the ALS\niterations. We also provide a parallel implementation of the accelerated\nALS-NCG algorithm in the Apache Spark distributed data processing environment,\nand an efficient line search technique as part of the ALS-NCG implementation\nthat requires only one pass over the data on distributed datasets. In serial\nnumerical experiments on a linux workstation and parallel numerical experiments\non a 16 node cluster with 256 computing cores, we demonstrate that the combined\nALS-NCG method requires many fewer iterations and less time than standalone ALS\nto reach movie rankings with high accuracy on the MovieLens 20M dataset. In\nparallel, ALS-NCG can achieve an acceleration factor of 4 or greater in clock\ntime when an accurate solution is desired; furthermore, the acceleration factor\nincreases as greater numerical precision is required in the solution. In\naddition, the NCG acceleration mechanism is efficient in parallel and scales\nlinearly with problem size on synthetic datasets with up to nearly 1 billion\nratings. The acceleration mechanism is general and may also be applicable to\nother optimization methods for collaborative filtering.","url_abs":"http://arxiv.org/abs/1508.03110v3","url_pdf":"http://arxiv.org/pdf/1508.03110v3.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":"algorithmic-acceleration-of-parallel-als-for","repo_url":"https://github.com/mbhynes/als-ncg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"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}