{"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/exploiting-sparsity-to-build-efficient-kernel","title":"Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation","arxiv_id":"1612.05729","date":"2016-12-17","proceeding":null,"authors":["Mirko Polato","Fabio Aiolli"],"abstract":"The increasing availability of implicit feedback datasets has raised the\ninterest in developing effective collaborative filtering techniques able to\ndeal asymmetrically with unambiguous positive feedback and ambiguous negative\nfeedback. In this paper, we propose a principled kernel-based collaborative\nfiltering method for top-N item recommendation with implicit feedback. We\npresent an efficient implementation using the linear kernel, and we show how to\ngeneralize it to kernels of the dot product family preserving the efficiency.\nWe also investigate on the elements which influence the sparsity of a standard\ncosine kernel. This analysis shows that the sparsity of the kernel strongly\ndepends on the properties of the dataset, in particular on the long tail\ndistribution. We compare our method with state-of-the-art algorithms achieving\ngood results both in terms of efficiency and effectiveness.","url_abs":"http://arxiv.org/abs/1612.05729v1","url_pdf":"http://arxiv.org/pdf/1612.05729v1.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":"exploiting-sparsity-to-build-efficient-kernel","repo_url":"https://github.com/makgyver/pyros","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}