collaborative-filtering
Get flash to fully experience Pearltrees
digg
newsvine
Introducing Plexo on Squidoo
A: Plexo is social bookmarking meets Digg meets the long tail. It allows anyone to host their rank list--to become editor in chief of one small slice of the best of world we live in. The best live jazz albums, the best blog posts about design, the stupidest members of Congress. Plexo lets you easily do what a blog can't... it lets you engage your audience and have them vote, every day if you like, on the most important issues to you. Q: I've never been to Squidoo before?
Item-to-item collaborative filtering
There appears to be a little confusion in some of the research literature on the earliest work on item-to-item collaborative filtering, a recommender algorithm that focus on items rather than users. The earliest work of which I am aware is:J. Michael Arrington (born March 13, 1970 in Huntington Beach, California) is a serial entrepreneur and the founder of TechCrunch, a blog covering startups and technology news. Arrington attended Claremont McKenna College (BA Economics, 1992) and Stanford Law School (JD, 1995) and practiced as a corporate and securities lawyer at two law firms: O’Melveny & Myers and Wilson Sonsini Goodrich... → Learn More A couple of days ago we posted screen shots of a new search interface being bucket tested by Google that lets users vote up or down on search results. The resulting interface was very Digg -like, and included a total vote count, etc. Today Adrian Pike, the CTO of startup Tatango , noticed that the interface changed yet again and now includes user comments.
Google Continues To Test A Search Interface That Looks More Like
Blog Archive » Collaborative Micro-filtering
A few days ago I read an interesting blog by Josh Porter at bokardo.com, on Facebook and Circles of Relationships . In it, he discusses how trust affects the relevance of the pieces of information we receive – honing in on the idea that information we receive from people we know has more inherent relevance than information we receive from strangers. I think “trust” is probably not the best word to describe what is being judged here – let’s face it, you probably trust a writer from the New York Times more than you trust your stoner roommate for certain things, especially factual information. But context , however, is a word that works well for describing what we need to judge the information we receive, especially for qualitative information, such as opinions. Context When we receive information from people we know, we have a lot more context for framing that information, than we do from someone we don’t know.
Written by Alex Iskold and edited by Richard MacManus 0 diggs digg It is no secret that we live in an information overload age. The explosion of new types of information online is a double-edged sword.
The Attention Economy: An Overview
Functioning Form - Web App Summit: Design Strategies for Recomme
January 27, 2007 by Luke Wroblewski At the UIE Web App Summit in Monterey, Rashmi Sinha walked through a series of design strategies for two types of recommender system designs: algorithmic systems prevalent in 2001 and social systems popular in 2006. Social recommenders (last.fm, flickr, YouTube, del.icio.us, etc.) have: user-generated content; long tail content; social networking; rich user experience; elements of play.
Findory
Findory was a personalized news site. The site launched in January 2004 and shut down November 2007. A reader first coming to Findory would see a normal front page of news, the popular and important news stories of the day.The Power Of Open Participatory Media And Why Mass Media Must Be
If a group has too many active members, then each one might be bombarded with hundreds of messages every day.
The Eaton Model of Collective Communication
The Eaton Model uses collective communication to build consensus in a non-confrontational way. Each of these three terms needs a short explanation. Collective communication is a novel technique of communication between groups.Item-based Collaborative Filtering Recommendation Algorithms
Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems.
maintained by Jun Wang Generally, collaborative filtering (CF) is any algorithm that filters information for a user based on a collection of user profiles. Users having similar profiles may share similar interests. For a user, information can be filtered in/out regarding to the behaviors of his or her similar users.
Collaborative Filtering Resources
Top Free Educational Resources :: qoolsqoo
Updated for the Spring 2010 season! Improved functionality and fewer ads! Welcome to the new QoolSqool!Google has created a new module for the personalized homepage that shows you recommendations, based on your search history, your location and on the search history of similar users.