{"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/preference-completion-large-scale","title":"Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons","arxiv_id":"1507.04457","date":"2015-07-16","proceeding":null,"authors":["Dohyung Park","Joe Neeman","Jin Zhang","Sujay Sanghavi","Inderjit S. Dhillon"],"abstract":"In this paper we consider the collaborative ranking setting: a pool of users\neach provides a small number of pairwise preferences between $d$ possible\nitems; from these we need to predict preferences of the users for items they\nhave not yet seen. We do so by fitting a rank $r$ score matrix to the pairwise\ndata, and provide two main contributions: (a) we show that an algorithm based\non convex optimization provides good generalization guarantees once each user\nprovides as few as $O(r\\log^2 d)$ pairwise comparisons -- essentially matching\nthe sample complexity required in the related matrix completion setting (which\nuses actual numerical as opposed to pairwise information), and (b) we develop a\nlarge-scale non-convex implementation, which we call AltSVM, that trains a\nfactored form of the matrix via alternating minimization (which we show reduces\nto alternating SVM problems), and scales and parallelizes very well to large\nproblem settings. It also outperforms common baselines on many moderately large\npopular collaborative filtering datasets in both NDCG and in other measures of\nranking performance.","url_abs":"http://arxiv.org/abs/1507.04457v1","url_pdf":"http://arxiv.org/pdf/1507.04457v1.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":"preference-completion-large-scale","repo_url":"https://github.com/dhpark22/collranking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"collaborative-ranking","task_name":"Collaborative Ranking"},{"task_slug":"matrix-completion","task_name":"Matrix Completion"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1507.04457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1507.04457"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dhpark22/collranking","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"7e7b579e2f6e1c41","entry":"pair_comp","repo":"dhpark22/collranking","repo_kind":"listed","path":"util/bin2comp.py","file_url":"https://github.com/dhpark22/collranking/blob/HEAD/util/bin2comp.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7e7b579e2f6e1c41"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}