{"id":835,"date":"2009-07-11T01:35:42","date_gmt":"2009-07-11T07:35:42","guid":{"rendered":"http:\/\/hunch.net\/?p=835"},"modified":"2009-07-11T01:35:42","modified_gmt":"2009-07-11T07:35:42","slug":"interesting-papers-at-kdd","status":"publish","type":"post","link":"https:\/\/hunch.net\/?p=835","title":{"rendered":"Interesting papers at KDD"},"content":{"rendered":"<p>I attended <a href=\"http:\/\/www.sigkdd.org\/kdd2009\/\">KDD<\/a> this year.  The conference has always had a strong grounding in what works based on the <a href=\"http:\/\/www.kddcup-orange.com\/\">KDDcup<\/a>, but it has developed a halo of workshops on various subjects.  It seems that KDD has become a place where the economy meets machine learning in a stronger sense than many other conferences.<\/p>\n<p>There were several papers that other people might like to take a look at.<\/p>\n<ol>\n<li><a href=\"http:\/\/research.yahoo.com\/Yehuda_Koren\">Yehuda Koren<\/a> <a href=\"http:\/\/research.yahoo.com\/files\/kdd-fp074-koren.pdf\">Collaborative Filtering with Temporal Dynamics<\/a>.  This paper describes how to incorporate temporal dynamics into a couple of collaborative filtering approaches.  This was also a best paper award.<\/li>\n<li><a href=\"http:\/\/www.eecs.tufts.edu\/~dsculley\/\">D. Sculley<\/a>, Robert Malkin, <a href=\"http:\/\/www.cs.utexas.edu\/users\/sugato\/\">Sugato Basu<\/a>, <a href=\"http:\/\/www.bayardo.org\/\">Roberto J. Bayardo<\/a>, <a href=\"http:\/\/www.bayardo.org\/ps\/kdd2009.pdf\">Predicting Bounce Rates in Sponsored Search Advertisements<\/a>.  The basic claim of this paper is that the probability people immediately leave (&#8220;bounce&#8221;) after clicking on an advertisement is predictable.<\/li>\n<li><a href=\"http:\/\/research.microsoft.com\/en-us\/people\/mcsherry\/\">Frank McSherry<\/a> and <a href=\"http:\/\/crypto.stanford.edu\/~mironov\/\">Ilya Mironov<\/a> <a href=\"http:\/\/research.microsoft.com\/pubs\/80511\/NetflixPrivacy.pdf\">Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders<\/a>.  The basic claim here is that it is possible to beat the baseline system in Netflix <i>and<\/i> preserve a nontrivial amount of user privacy.  It&#8217;s the first demonstration I&#8217;ve seen of this sort, and it&#8217;s particularly impressive they used a strong algorithm-independent definition of privacy which <a href=\"http:\/\/research.microsoft.com\/en-us\/people\/dwork\/\">Cynthia Dwork<\/a> first stated.<\/li>\n<\/ol>\n<p>KDD also <a href=\"http:\/\/kdd09.crowdvine.com\/calendar\">experimented this year<\/a> with <a href=\"http:\/\/www.crowdvine.com\/home\">crowdvine<\/a> which was interesting.  Compared to <a href=\"http:\/\/mark.reid.name\/\">Mark Reid<\/a>&#8216;s <a href=\"http:\/\/www.conflate.net\/icml\/\">efforts with ICML<\/a>, they managed to get substantially more participation.  There seemed to be two reasons: the conference organizers more deeply integrated and encouraged the use of crowdvine, and crowdvine has certain handy additional uses&#8212;you can create your own personal schedule for instance, which incidentally provides some vague global notion of the popularity of various papers.  The biggest drawback I found was that the papers themselves were <i>not<\/i> integrated into the website.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I attended KDD this year. The conference has always had a strong grounding in what works based on the KDDcup, but it has developed a halo of workshops on various subjects. It seems that KDD has become a place where the economy meets machine learning in a stronger sense than many other conferences. There were &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/hunch.net\/?p=835\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Interesting papers at KDD&#8221;<\/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":[29],"tags":[],"class_list":["post-835","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"_links":{"self":[{"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts\/835","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=835"}],"version-history":[{"count":0,"href":"https:\/\/hunch.net\/index.php?rest_route=\/wp\/v2\/posts\/835\/revisions"}],"wp:attachment":[{"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=835"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=835"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hunch.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=835"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}