Drink recommendation

Within the last ten days I've been in Asia, Europe and North America. I've taken jetlag to a new level. Usually there is a reference point, you can say, "It's past midnight in the Netherlands at the moment, my internal clock thinks it's past my bedtime and that's why I am so tired." Now I have no clue why time my internal clock reads.

At the grocery store, I just picked out a four pack of energy drink in order to try to jump start myself and get re-aligned with the cycle of the sun at my current location. I stood for ten minutes in front of the selections, looking at the cans and then reading the labels. I wanted something not too expensive, sugar free and also with guarana. A Brazilian colleague had recommended guarana as one of the best "pick up" ingredients you can get in an energy drink.

What I could use is a good drink recommendation system. The Asian part of this odyssey took place in Tokyo, and the following video was what YouTube there listed as a popular video. It had received 44466 views in the one day since it had been uploaded.

1 dag geleden 44466 keer bekeken



It is a news report on a drink vending machine (a Tokyo fixture) that recommends drinks by taking your picture and doing a little bit of multimedia content analysis that gives it clues as to your age and gender.

In my current situation, age and gender wouldn't have been enough. Rather the system would need information about my internal state -- the camera would have to have noticed the unfocused glaze of my tired eyes. In this situation, internal-state information could be inferred if the system had access to information about my geo-coordinates within the last ten days. Access to a recent history of my sleeping-waking pattern would provide an even better source of evidence.

However, another key bit of information, that would be critical to get to the correct drink would be that at the moment I do not want to be tired. I can't be tired. I don't want something that will relax me -- no chamomile, not yet. I need to work.

The bottom line is clear: barring a system that has access to all that information and the ability to use it in the right way, the Brazilian colleague remains the best source of drink recommendations.

And it looks like the drink is working already, since I have already reached a level of alertness to attempt a blog post.

Where's Wikipedia?

The ACM Multimedia Grand Challenge is a high-adrenaline event where researchers from the Multimedia community compete against each other to develop the best solutions to problems posed by industry. For example, Google formulated two challenges, Video Genre Classification and Personal Diaries, in this year's competition.

Today in Tokyo at Interspeech 2010, I stopped to chat with last year's Grand Challenge winner, who is competing once again this year. I was struck anew by the realization that in the pressure-cooker of the Grand Challenge, creativity, raw intelligence, technical competence, competitive drive and off-beat thinking gives rise to lines of attack that might never have emerged in a traditional R&D setting. Such solutions stand to benefit us all.

But is it really only industry who should be formulating the challenges for such competitions? Where, for example, is Wikipedia? If there is any major player in the Internet information arena that deserves a crowd-sourced solution from the research community, it is Wikipedia, the knowledge resource homegrown by collaborative effort.

Wikipedia does truly inspire the research community. Very recently I've witnessed up close how fired up scientists get about Wikipedia. The Tribler team, who sit on the ninth floor of our building, have been sinking unbelievable time and effort into the development of the Swarmplayer V2.0. Their dedication is inspiring and their incredible belief in the power of a distributed solution for videos on Wikipedia is infective.

Datasets from Wikipedia have been used by multiple benchmarking initiatives such ImageCLEF and INEX as well as in MediaEval, the benchmark I co-ordinate. We certainly enjoyed coming up withour own Wikipedia-related task. However, it would be great to hear directly from the Wikimedia Foundation, in the form of a Grand Challenge, what problems they see on the horizon in the next 2-5 years for which the research community could be helpful in generating solutions. The Challenge takes the form of a simple textual description of the problem and researchers do the rest, presenting the solution in form of a system or system demo and a paper describing it.

There's a lot out there of course that I don't know about. For example, just read this post on the ECML PKDD 2010 Data Challenge: Measuring Web Data Quality. But I've never seen a clear Challenge originating from the Wikipedia community and published for the research community.

One aspect that researchers need to think seriously about, however, is the form in which solutions for Wikipedia or developed using Wikipedia data are published. ACM Multimedia Proceedings are not an open access publication. It's a contradiction to carry out research on a free knowledge resource and publish results under conventional copyright. Peer-reviewed open access journals such as the Journal of Digital Information should be preferred when publishing results obtained using Creative Commons licensed data.

Maybe that's actually one Challenge that the Wikimedia Foundation actually has to offer the research community: challenging us to breaking the habit of creating solutions in a rush of creative joy and technical muscle, and then publishing them where they cannot be accessed by everyone.