{"id":229,"date":"2019-10-17T03:52:25","date_gmt":"2019-10-17T03:52:25","guid":{"rendered":"http:\/\/wordpress.cs.vt.edu\/cscw2019\/?p=229"},"modified":"2019-10-17T03:52:27","modified_gmt":"2019-10-17T03:52:27","slug":"the-dawn-of-feeds-reading-reflection-4","status":"publish","type":"post","link":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/2019\/10\/17\/the-dawn-of-feeds-reading-reflection-4\/","title":{"rendered":"The dawn of feeds: Reading reflection #4"},"content":{"rendered":"\n

[P]eople who agreed in the past are likely to agree again <\/p><\/blockquote>\n\n\n\n

So simple, and yet this heuristic is the bedrock of every modern recommendation algorithm.  This is \u201cBecause you watched X\u201d on Netflix and \u201cOthers who viewed Y also viewed\u201d on Amazon, and the basic idea behind our Facebook feeds.  Behold: collaborative filtering<\/em>.<\/p>\n\n\n\n

Before, there were e-bulletin boards and similar means of bringing articles from disparate sources to one app.  The signal to noise ratio was low and there was only software that allowed you to set filters manually, e.g. I want to see articles that have the word bird<\/em> in the title, and not articles with cat<\/em> in the title (content filtering); I want to see articles by Jane Doe, and not articles by John Deer or anyone who\u2019s ever written with John Deer (social filtering); or I want to see articles that my friend Abdul liked, and none that my enemy Karen liked (the most primitive collaborative filtering).<\/p>\n\n\n\n

GroupLens was an open source architecture developed by Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl that filtered netnews for you based on the judgements of other users.  You don\u2019t have to know the other users; you don\u2019t have to know which users have similar (or dissimilar) tastes to you; you don\u2019t have to publicly announce your tastes\u2014you (and everyone else) have only to mark articles you\u2019ve read on a scale of 1 (didn\u2019t like) to 5 (liked a lot), use a pseudonym if you want, and GroupLens takes the wheel.  People with positively (or negatively) correlated tastes in articles to yours translate to bigger (or smaller) weights in the matrix, and there you have it.<\/p>\n\n\n\n

This is what goes on behind every \u201cfeed\u201d on our screens now\u2014Facebook, Google news, Twitter, ads everywhere, you name it. Although we don\u2019t usually explicitly give our approval or disapproval of items like in GroupLens, our scrolling and searching patterns tells the algorithms everything they need to know.  If I follow one Star Trek actor on Twitter, I will be served up tweets that people who also follow that actor also \u201cliked.\u201d<\/p>\n\n\n\n

I haven\u2019t spent nearly enough time on Wikipedia yet, but I don\u2019t think they use any sort of collaborative recommendation system, and that might be on purpose\u2014it would be better if people didn\u2019t<\/em> only read and edit articles that similar-minded users read and edited.  The front page features the same daily article for everyone (today: Battle of Neville’s Cross<\/a><\/strong>) and the same In the News<\/em> section, and I like that. But I can imagine a system that noticed if, for instance, I edit in two languages, and noticed that a similar editor to me (maybe someone who also edits in those two languages and has edited similar pages to me) just edited some page, and notified me.  That might be helpful in keeping pages up to date, especially pages in languages with fewer editors. I don\u2019t want to know if just any random page is edited, but if it\u2019s edited by someone with similar \u201ctaste\u201d in articles to my own, there\u2019s a fair chance that I\u2019d want to make an improvement to that page as well, or edit the corresponding page in the other language.<\/p>\n","protected":false},"excerpt":{"rendered":"

[P]eople who agreed in the past are likely to agree again So simple, and yet this heuristic is the bedrock of every modern recommendation algorithm.  This is \u201cBecause you watched X\u201d on Netflix and \u201cOthers who viewed Y also viewed\u201d on Amazon, and the basic idea behind our Facebook feeds.  Behold: collaborative filtering. Before, there…<\/p>\n","protected":false},"author":266,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-229","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/posts\/229","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/users\/266"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/comments?post=229"}],"version-history":[{"count":3,"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/posts\/229\/revisions"}],"predecessor-version":[{"id":232,"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/posts\/229\/revisions\/232"}],"wp:attachment":[{"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/media?parent=229"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/categories?post=229"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress.cs.vt.edu\/cscw2019\/wp-json\/wp\/v2\/tags?post=229"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}