{"id":300,"date":"2007-11-14T14:53:50","date_gmt":"2007-11-14T20:53:50","guid":{"rendered":"http:\/\/hunch.net\/?p=300"},"modified":"2007-11-14T14:53:50","modified_gmt":"2007-11-14T20:53:50","slug":"bellkor-wins-netflix","status":"publish","type":"post","link":"https:\/\/hunch.net\/?p=300","title":{"rendered":"BellKor wins Netflix"},"content":{"rendered":"

… but only the little prize. The BellKor team<\/a> focused on integrating predictions from many different methods. The base methods consist of:<\/p>\n

    \n
  1. Nearest Neighbor Methods<\/li>\n
  2. Matrix Factorization Methods (asymmetric and symmetric)<\/li>\n
  3. Linear Regression on various feature spaces<\/li>\n
  4. Restricted Boltzman Machines<\/li>\n<\/ol>\n

    The final predictor was an ensemble (as was reasonable to expect), although it’s a little bit more complicated than just a weighted average—it’s essentially a customized learning algorithm. Base approaches (1)-(3) seem like relatively well-known approaches (although I haven’t seen the asymmetric factorization variant before). RBMs are the new approach.<\/p>\n

    The writeup<\/a> is pretty clear for more details.<\/p>\n

    The contestants are close to reaching the big prize, but the last 1.5% is probably at least as hard as what’s been done. A few new structurally different methods for making predictions may need to be discovered and added into the mixture. In other words, research may be required.<\/p>\n","protected":false},"excerpt":{"rendered":"

    … but only the little prize. The BellKor team focused on integrating predictions from many different methods. The base methods consist of: Nearest Neighbor Methods Matrix Factorization Methods (asymmetric and symmetric) Linear Regression on various feature spaces Restricted Boltzman Machines The final predictor was an ensemble (as was reasonable to expect), although it’s a little … <\/p>\n

    Continue reading “BellKor wins Netflix”<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[31,29],"tags":[],"class_list":["post-300","post","type-post","status-publish","format-standard","hentry","category-competitions","category-machine-learning"],"_links":{"self":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts\/300","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=300"}],"version-history":[{"count":0,"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts\/300\/revisions"}],"wp:attachment":[{"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=300"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=300"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=300"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}