usability challenges of web 2.0 applications

In this month’s Interactions feature article Is Usability Obsolete?, Katie Minardo Scott raises a number of interesting issues about usability in a Web 2.0 world:

Usability can no longer keep up with computing: The products are too complex, too pervasive, and too easy to build. And in our absence, users and engineers are beginning to take over the design process. Five trends demonstrate the growing gap between usability theory and commercial practice – the “new realities” of computing haven’t been truly embraced by the usability community. The trends are, at a minimum, making traditional usability more difficult, if not irrelevant in the new paradigm.

I am certainly no usability expert, rather just curiously interested in the field.

Modern software, particularly social web and/or mobile applications definitely appear to pose new and different challenges – in many respects. The example of a location-dependent web service that incorporates user-contributed data seems particularly well chosen. It involves many different aspects, each of which could make or break the user experience.

As these new classes of applications are developed, researched and understood better, software development approaches also change, incl. introduction of new tools, algorithms and practices.

I don’t see, why the same couldn’t also happen for usability work. A lot of basic usability information has become near-common knowledge. According to the author:

[...] with the growth of the Web and usability, clients are likely to know the underlying usability principles, be familiar with the core heuristics, and have already solved the obvious “gotchas” in their products. They may even have in-house usability departments, labs, and protocols. Fewer and fewer clients need to be reminded of the basics. The heuristics we test for and baseline against are pervasive; at some level, we’ve put ourselves out of business.

On some level, I am inclined to take the above quote from the article as an encouraging sign of a maturing field. System administrators often strive to automate as many of their tasks as possible. Similarly, developers come up with automation tools and essentially means to achieve more in less time. Examples from other fields could easily be provided.

This can often result in people being able to start focusing on more advanced problems, which may not have been practical or feasible before. I bet a lot of people find it refreshing to focus less on educating their clients about the needs for basic work and rather focus on addressing harder problems.

IEEE Internet Computing’s interesting editorial calendar

I was recently looking over the editorial calendar of IEEE Internet Computing and I must say, I am going to look forward to every single issue until next June! Here is a brief list of the topics:

• Emerging Internet Technologies and Applications for E-Learning – July/August 2009
• Cloud Computing – September/October 2009
• Unwanted Traffic – November/December 2009
• Social Computing in the Blogosphere – March/April 2010
• Rich Internet Applications – May/June 2010

Calls for papers are still open for the last two issues. This is an excellent publication with high quality content for researchers and developers in the Internet space. I wish they would move to a monthly format and add a lively and content-rich web community.

More netflix-style competitions?

In The Million Dollar Programming Prize, Robert M. Bell, Jim Bennett, Yehuda Koren and Chris Volinsky give an account of one team’s experiences participating at the Netflix Prize competition.

The authors are pointing out some interesting findings towards the end of the article.

Now that the confetti has settled, we have a chance to look back on our work and to ask what this experience tells us. First, Netflix has incorporated our discoveries into an improved version of its algorithm, which is now being tested. Second, researchers are benefiting from the data set that the competition made available, and not just because it is orders of magnitude larger than previous data sets. It is also qualitatively better than other data sets, because Netflix gathered the information from paying customers, in a realistic setting. Third, the competition itself recruited many smart people in this line of research.

In any case, the new blood promises to quickly improve the state of the art. Such competitions have invigorated other fields. The various X Prizes that have been offered for advances in genomics, automotive design, and alternative energy have shown an excellent return: By some accounts the recent $10 million Ansari X prize, awarded for suborbital spaceflight, generated $100 million of private investment in space travel.

(From the article.)

It seems like a number of other companies could observe these lessons and offer their own versions of such competitions. Amazon.com or YouTube could make large data sets available and pose improvement of their recommendation algorithms as a challenge to the public developer community. Google could provide a data set of pages, their links, metadata, etc. and challenge developers to improve on result rankings. I am thinking a lot of companies are working on interesting, hard problems and may be able to allow outside developers and scientists to try their hands on those as well. I am wondering though how broadly the competition format can be used.

Which problems and areas of research are suitable for this approach?

Trying out Hunch

Caterina Fake, of Flickr fame recently announced Hunch and fairly quickly generated some healthy buzz. Daniel Tunkelang, author of The Noisy Channel, generously invited me to try out the service. I checked it out this weekend and here are some of my thoughts.

Hunch is essentially a crowdsourced decision support system:

In 10 questions or less, Hunch will offer you a great solution to your problem, concern or dilemma, on hundreds of topics. Hunch’s answers are based on the collective knowledge of the entire Hunch community, narrowed down to people like you, or just enough like you that you might be mistaken for each other in a dark room. Hunch is designed so that every time it’s used, it learns something new. That means Hunch’s hunches are always getting better.

(About – Hunch.com)

Conceptually I enjoy that idea quite a lot.

I worked through a number of different question sequences. Hunch arrived at the conclusion that I should be an engineer, an entrepreneur or a computer programmer. Furthermore, I use grammar at least at a sixth grade level and yes, it is time to switch to a Mac. That’s all pretty encouraging.

The software is of course currently in closed beta and has not been heavily used by large numbers of people. Applications of collective intelligence tend to really play their strengths, once lots of people are involved. I am curious to see how the quality of the content and accuracy of the answers is going to change over time.

A few discussion points:

• What if the actual best answer is not part of the available answer set of a given decision tree? This is mostly a problem if I don’t know much about the subject matter.
• In how far do personal preferences play into decisions here? If someones answered an exhaustive number of individual questions on the homepage, would it make sense to offer some decisions after only one or two specific questions?
• A few times when presented with the possible answers to a question, I did not see a good fit. Maybe I did not like any of them or more than one option seemed correct. Maybe one option seems reasonable in some context, but a different one in a different context. On the one hand, this really is where the advantage if crowdsourcing comes in. On the other though, it leaves me wondering: In how far is this going to be used for complex decisions and how much is the 10-question limit per decision going to matter?

Again though, I do enjoy the concept and will continue experimenting with this.