GroupLens Research

Fun blog entry at freakonomics summarizing the querty/dvorak debate.  I love the way they summarize the different back-and-forth arguments.  Sadly, they don't leave us with a ground truth: what's better?!  

Paul Graham is one of my favorite tech essayists.  His essays on startup companies, Lisp, and -- especially -- American high schools are smart, funny, and insightful.  On these topics, even when I think he's wrong, I learn something.

An interesting article discusses the problem in the reddit community that the community has wide agreement on a variety of issues, and that therefore only articles with the "correct" viewpoint on those issues get many votes.  (Similar problems apply to the other social news sites like slashdot and digg.)

The off-the-cuff response from the recommender community might be "let's solve the problem by creating personalized reddit".  In this world, everyone would read the articles he or she was most interested in, creating many overlapping communities of interest. A concern with this approach is that social psychology suggests that by making it easy for people to only talk with others with whom they agree, we would be creating a world that would emphasize our differences, amplifying them over time, balkanizing the community of news readers.  For instance, all of the atheists would only read articles that support their views, and would become increasingly resistant to theist views -- and eventually unable to even find common ground for discussion with theists.  

An alternative would be to find a way to create a community news reader that would simultaneously support personalization and encourage the sharing of opinions. What would such a news reader look like? How would it use recommenders in a novel way?

Chipmark (www.chipmark.com) is a cool bookmarking site that lets you share your bookmarks between multiple browsers seamlessly.  (Bias alert: I've been advisor to the chipmark group for the past three years.)  Chipmark is written entirely by University of Minnesota undergrads, and distributed free of cost either as a server the students maintain, or as open source code that anyone is welcome to use to run his own server.  The most recent release of chipmark supports shared folders among buddies, and will be out Real Soon Now.  Check it out!

Here is a thoughtful blog post about why people choose a programming language to use.  The key question is why many people's favorite languages, like Lisp, Smalltalk, or ML, have not won the battle for the main programming language in use.  The key answers are (in my order, not the article's) libraries, IDEs, documentation, and performance.

John Langford recently blogged about researchers preference to cite recent research. He calls this tendency "the forgetting" of prior work. John suggests a number of reasons recent work may be remembered (including "Dead men don't reject your papers for not citing them").

Jonathan 'Wolf' Rentzsch has an interesting blog on the theme that [[Programmers Don't LIke to Code | http://rentzsch.com/notes/programmersDontLikeToCode]].  His argument is that programmers like to solve problems, not to develop code to solve those problems.  He gives a number of great arguments: programmers choose tools that make it easier to get their jobs done (languages and libraries that require *less* code), and that programmers find it frustrating when they are building something and they have to build lots of piddly stuff that doesn't really advance their cause di

We have often asked the question: in an online system, does participation in social features cause greater attachment to the system, or is it merely correlated with greater attachment? This research addresses this question directly, with powerful results indicating a causal relationship:

http://www.futureofcommunities.com/?p=12

Max

Joshua Porter has written an article on the how recommendation systems are changing the Web. He lists several benefits and drawbacks stemming from these technologies. It is cool to observe that our group is actively researching to improve three of the four "drawbacks" that he lists:

• Dan Frankowski is working on Recommenders Everywhere - one attempt to make recommendation systems easier to set up
• Dan Cosley is working to providing tools that help communities build and maintain good databases
• Tony Lam is working to understand shilling in recommender systems
  

See http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=6518