{"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/turbo-cf-matrix-decomposition-free-graph","title":"Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation","arxiv_id":"2404.14243","date":"2024-04-22","proceeding":null,"authors":["Jin-Duk Park","Yong-Min Shin","Won-Yong Shin"],"abstract":"A series of graph filtering (GF)-based collaborative filtering (CF) showcases state-of-the-art performance on the recommendation accuracy by using a low-pass filter (LPF) without a training process. However, conventional GF-based CF approaches mostly perform matrix decomposition on the item-item similarity graph to realize the ideal LPF, which results in a non-trivial computational cost and thus makes them less practical in scenarios where rapid recommendations are essential. In this paper, we propose Turbo-CF, a GF-based CF method that is both training-free and matrix decomposition-free. Turbo-CF employs a polynomial graph filter to circumvent the issue of expensive matrix decompositions, enabling us to make full use of modern computer hardware components (i.e., GPU). Specifically, Turbo-CF first constructs an item-item similarity graph whose edge weights are effectively regulated. Then, our own polynomial LPFs are designed to retain only low-frequency signals without explicit matrix decompositions. We demonstrate that Turbo-CF is extremely fast yet accurate, achieving a runtime of less than 1 second on real-world benchmark datasets while achieving recommendation accuracies comparable to best competitors.","url_abs":"https://arxiv.org/abs/2404.14243v1","url_pdf":"https://arxiv.org/pdf/2404.14243v1.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":"turbo-cf-matrix-decomposition-free-graph","repo_url":"https://github.com/jindeok/turbo-cf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-amazon-book","task":"Recommendation Systems","dataset":"Amazon-Book","model":"Turbo-CF","rank_in_archive_order":6,"of":16,"metrics":{"Recall@20":"0.0693","nDCG@20":"0.0574"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-yelp2018","task":"Recommendation Systems","dataset":"Yelp2018","model":"Turbo-CF","rank_in_archive_order":7,"of":11,"metrics":{"NDCG@20":"0.0574","Recall@20":"0.0693"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.14243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14243"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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