Harold asks: do we suffer from information overload or do we have the wrong tools? Clearly, email is inefficient. It is like cars: everybody gets stuck in traffic.

To cope, I answer and compose emails only once a day, on a schedule (after 4pm). I check and prune my email regularly however.

How do you cope with email?

See also Improving your intellectual productivity by accepting chaos and How to manage email (Inbox Zero).

Active Time is a free MacOS application keeping track of how much time I spend in various software applications — automatically! My bet is that most of my time is spent in a browser, but I want to get hard numbers.

We are in an era of overabundance. Many of the problems we face — spam, information overload, obesity, pollution — are actually the result of overabundance.

Scientists need new strategies:

• Create fast, discard faster.
• Aim for quality. When people have too much content, they want quality.
• Focus and live in niches.
• Produce shorter papers. People want to learn a specific facts. Make it easy for them to find them.
• Use formats that are easy to index. Paper is terrible. Slides, voice and video are not very good. Digital text in simple formats is better.
• Make your work easy to find: nobody has time to mail order.
• Be agile: always be ready to change focus. There are just too many new opportunities!

Reference: Ann Blair, Reading Strategies for Coping With Information Overload ca. 1550-1700, Journal of the History of Ideas 64.1 (2003) 11-28.

I guess many people are getting Wiis right about now. I have had mine for a few months. Here are my recommendations:

• The Sims 2: Castaway. You do not need to know what the sims are. If you can cope with games requiring planning, some puzzles, but relatively little action, this game is for you. Reminds me of the TV show “Lost.”
• Super Mario Galaxy. A relaxing and fun platform game. Very innovative, in a strange way. Reminds me of the Little Prince by Saint-Exupéry.
• Resident Evil 4. This is an older strategic/action game, retrofitted to use the Wii controls. Well done and scary!
• Medal of Honor: Heroes 2. I think that first-person shooters are getting old, but this one uses the Wii controls to good effect. Oddly immersive.
• Opera Web browser for the Wii. I am typing this using my Wii right now. You can purchase the browser in the online boutique directly on the Wii. Well worth the money. Opera is a good browser.

• I will not use negations…
• I will avoid UA (useless acronyms).
• When appropriate, my writing will be in an active voice…
• I will very much try to avoid carefully needless words in my writing.
• I will employ uncomplicated terms.

Here is a call to my readers: what annoys you about my writing? I vow to improve!

ICDM’08 — the 8th IEEE International Conference on Data Mining — will be held in Pisa, Italy. ICDM is a big and prestigious conference on Data Mining. It has some pretty good workshops, but the list of workshops is not yet available. See the workshops they had last year.

(If you do not mind useless advice, do check the workshops first if you are going to prepare a paper. I see very little evidence in recent years that the papers accepted at the main conference have more impact.)

As a scientist, it is important to question your assumptions. So far, most of the hard Computer Science research on collaborative filtering has used static data sets such as Netflix. Specifically, it is assumed that the recommender systems do not impact the ratings and what items get rated. A related assumption is that polls do not change how people vote (thanks to Peter for this observation).

Yet, people’s preferences are often constructed in the process of elicitation. That is, collaborative filtering is a nonlinear problem: ratings feed into the recommender system which helps to determine what people rate, which, in turn, feeds back into the recommender system…

How could a researcher take this into account? It would be too expensive to try to simulate e-commerce sites with volunteers. We need to submit simulated users to a recommender system. The usefulness of the recommendations is a tricky thing to measure and cross-validation errors are probably not what you want to study exclusively, diversity might be an important factor too.

Note 1: If someone out there know how to simulate users (something I do not know how to do), please get in touch! I have no idea how to do sane user modelling and I need help!

Note 2: Peter also once pointed me to the Iterated Prisoner’s Dilemma problem as something related.