{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T10:53:17Z","timestamp":1776768797411,"version":"3.51.2"},"reference-count":10,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2007,12,1]],"date-time":"2007-12-01T00:00:00Z","timestamp":1196467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2007,12]]},"abstract":"<jats:p>The Netflix Prize is a collaborative filtering problem. This subfield of machine learning became popular in the late 1990s with the spread of online services that used recommendation systems (e.g. Amazon, Yahoo! Music, and of course Netflix). The aim of such a system is to predict what items a user might like based on his\/her and other users' previous ratings. The Netflix Prize dataset is much larger than former benchmark datasets, therefore the scalability of the algorithms is a must. This paper describes the major components of our blending based solution, called the Gravity Recommendation System (GRS). In the Netflix Prize contest, it attained RMSE 0.8743 as of November 2007. We now compare the effectiveness of some selected individual and combined approaches on a particular subset of the Prize dataset, and discuss their important features and drawbacks.<\/jats:p>","DOI":"10.1145\/1345448.1345466","type":"journal-article","created":{"date-parts":[[2008,2,28]],"date-time":"2008-02-28T14:02:33Z","timestamp":1204207353000},"page":"80-83","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":91,"title":["Major components of the gravity recommendation system"],"prefix":"10.1145","volume":"9","author":[{"given":"G\u00e1bor","family":"Tak\u00e1cs","sequence":"first","affiliation":[{"name":"Sz\u00e9chenyi Istv\u00e1n University, Gy\u00f6r, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Istv\u00e1n","family":"Pil\u00e1szy","sequence":"additional","affiliation":[{"name":"Budapest University of Technology and Economics, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Botty\u00e1n","family":"N\u00e9meth","sequence":"additional","affiliation":[{"name":"Budapest University of Technology and Economics, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Domonkos","family":"Tikk","sequence":"additional","affiliation":[{"name":"Budapest University of Technology and Economics, Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2007,12]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"3","volume-title":"Proc. of KDD Cup and Workshop","author":"Bennett J.","year":"2007","unstructured":"J. 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Conf. on Computational Science and Its Applications, Part II., number 3044 in Lecture Notes in Computer Science Series, pages 988--997. Springer, 2004."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/223904.223929"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102441"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/192844.192905"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273596"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_2_1_10_1","volume-title":"Statistical Methods","author":"Snedecor G. W.","year":"1980","unstructured":"G. W. Snedecor and W. G. Cochran . Statistical Methods . Iowa State University Press , 7 th edition, 1980 . G. W. Snedecor and W. G. Cochran. Statistical Methods. 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Advances in Neural Information Processing Systems"}],"container-title":["ACM SIGKDD Explorations Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1345448.1345466","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1345448.1345466","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T20:22:21Z","timestamp":1750278141000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1345448.1345466"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,12]]},"references-count":10,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2007,12]]}},"alternative-id":["10.1145\/1345448.1345466"],"URL":"https:\/\/doi.org\/10.1145\/1345448.1345466","relation":{},"ISSN":["1931-0145","1931-0153"],"issn-type":[{"value":"1931-0145","type":"print"},{"value":"1931-0153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,12]]},"assertion":[{"value":"2007-12-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}