{"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/weighted-svd-matrix-factorization-with","title":"Weighted-SVD: Matrix Factorization with Weights on the Latent Factors","arxiv_id":"1710.00482","date":"2017-10-02","proceeding":null,"authors":["Hung-Hsuan Chen"],"abstract":"The Matrix Factorization models, sometimes called the latent factor models,\nare a family of methods in the recommender system research area to (1) generate\nthe latent factors for the users and the items and (2) predict users' ratings\non items based on their latent factors. However, current Matrix Factorization\nmodels presume that all the latent factors are equally weighted, which may not\nalways be a reasonable assumption in practice. In this paper, we propose a new\nmodel, called Weighted-SVD, to integrate the linear regression model with the\nSVD model such that each latent factor accompanies with a corresponding weight\nparameter. This mechanism allows the latent factors have different weights to\ninfluence the final ratings. The complexity of the Weighted-SVD model is\nslightly larger than the SVD model but much smaller than the SVD++ model. We\ncompared the Weighted-SVD model with several latent factor models on five\npublic datasets based on the Root-Mean-Squared-Errors (RMSEs). The results show\nthat the Weighted-SVD model outperforms the baseline methods in all the\nexperimental datasets under almost all settings.","url_abs":"http://arxiv.org/abs/1710.00482v1","url_pdf":"http://arxiv.org/pdf/1710.00482v1.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":"weighted-svd-matrix-factorization-with","repo_url":"https://github.com/demianbucik/collaborative-filtering-recommender-systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"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}