How does Google Suggest work?

With the help of this tool: JavaScript Code Improver, I was able to figure this out…

The result here is run through JavaScript’s eval function.


SHA-1 broken

I noticed reading this article, and the one on Bruce Schneier’s blog, that the only source backing up Bruce Schneier on this SHA-1 crack is Bruce Schneier. Bruce, Bruce. Is that responsible security? Here’s a guy that preaches responsibility about security, and all this SHA-1 crack talk with no backup source is just stirring up FUD.

I believe him, but it makes me wonder what would happen if someone believed me.

OK, here’s my big announcement of the day: I broke 2048 bit RSA. I discovered a technique to factor very large primes using my quantum computer. Oh yeah, it’s all detailed in my upcoming paper.. which you can’t get.

I’m in the Barometer

Pretty exciting!

How did this happen? I contacted some guys at KBVR-FM (the campus radio station) about a while back. Turns out one of them, Andrew Nealon, is also a wordsmith for the school newspaper and he liked the site so much he offered to do an article in addition to spreading the word at the radio station. So today the Barometer ran his article on genielab.

I’m pleased with the article, I think it turned out great. But I gotta make one minor correction so I can save face amoung my computer science cohorts. The article says “ [is] one of the first active recommendation systems on the Internet.” While I guess that statement is true (based on your definition of “one of the first”), it still makes me sound a little presumptuous. What I think the author was trying to get at was a part of our conversation where we were talking about active vs. passive recommender systems. I was explaining that GenieLab was active, and that the popular music recommendation systems today (audioscrobbler, musicmobs, etc) are all passive. With GenieLab, you just tell it what you like. With a passive system, it watches and tries to learn what you like. There are other active music recommendation systems out there today, but I think GenieLab is pretty far ahead of them in terms of usability and the quality of recommendations. Ooops, I still sound presumptuous. My bad. ;-p

Corporations Sue Google over Results for Competitors

From NPR, via Slashdot

Internet search engine Google says it is deciding whether to appeal a decision by a French court that has implications for its lucrative advertising model. The court ordered Google to stop displaying ads for competitors of Louis Vuitton when users searched for the luxury goods maker. There are similar cases being brought in the United States.

What if the customer was specifically searching for Louis Vuitton knock-offs? Google is an independent search engine, and I would think “ad searches” should be encompassed by that definition. If Google helps someone find a Louis Vuitton knock-off purse and that’s what they were after it doesn’t seem fair for LV to sue Google over that.

If you buy that argument then I think it boils down to this: What was the intent of the customer when they entered their search terms? Obviously, Google can’t know the intent of the customer.

Is there a trademark issue in here? Can you prevent competitors from using your trademark? Can you place an ad in a newspaper that says, “Knock-off Louis Vuitton Purses for Cheap”? What Google does is kind of like placing an ad like that in a newspaper right next to a series of articles about Louis Vuitton.

Placing an ad for a competitor next to an article about a particular company isn’t illegal; it happens all the time. The only reason it doesn’t happen more often is newspapers and magazines generally consider it a poor practice: it hurts the reputation of the newspaper. Using the trademark name of a competitor isn’t illegal either: if you use it correctly. You can’t use the trademark in any way that diminishes their brand or confuses/misleads the customer.

For example, you couldn’t use the “LV” logo in an add for Knock-off LV purses, that clearly diminishes their brand. Advertising “Knock-off Lousis Vuitton” purses… I’m not sure about that. That’s kind of like advertising “Immitation Pepsi at low prices” instead of “Coke: It’s better than Pepsi.” But this is still very different from what Google is doing: they are trigging a competitors ad based on a keyword search that may contains a trademark. The ad doesn’t necessarily contain any reference to LV.

Can you imagine the fallout from this? If everyone started suing Google over this then they would need to maintain a database of trademarks and start red-flagging ads that appear on trademark searches. Google might even need to go so far as to investigate every AdWords customer to find out what trademarks they are competitors of. Could Google just offload this onto their AdWords customers? What if you signed a waiver that said that you wouldn’t write AdWords that trigger on keyword searches for your competitor? Oh my, what a mess that would be.

I don’t think what Google is doing should be illegal. Just like the newspaper/magazine example above, it should be considered poor taste. Triggering ads based on competitor keyword searches hurts Google’s relationship with the primary trademark holder. Google won’t be getting any discounts on $200 LV bags for now, they’ll just have to go into downtown SF and pick up the knock-offs for $10. 🙂

Passive vs. Active Recommendation Systems

I had an email exchange with Chris Anderson a couple of days ago, author of the Long Tail blog (and of Wired Magazine fame) about recommendation systems. After sending Chris a pointer to my music recommendation system, he had this to say:

    Thanks for the pointer. A quick bit of feedback: I’ve argued that the only recommendation services that will really scale are passive ones, that watch what people do rather than make them actively rate titles. Basically, I’m lazy and there are plenty of music recommendation services that don’t make me lift a finger. Is there any way you can make it zero-effort?

To which I replied:

    I think you live too far in the future. One day all of our devices will play digital music and keep track of how many times we play song, songs we tend to skip over, your ratings for songs, etc. Until then, the only tools that really do that (well) are the iPod and iTunes.

    Passive systems require you to already have your “profile” built, or at least “in the works.” A passive music recommendation system will work with all of your digital music players, collate everything they know about you, and then make recommendations. Today, I think very few people listen to music exclusively on their iPod or in iTunes. Even with the people who do use these players exclusively I still don’t believe their players have a very good idea of their listening tastes. You alluded to something like this in your “case against the shuffle” posting: what about the ghosts of “good” tastes past? The songs you forgot you liked? The songs that you hear on the radio, on TV, on CD, or at a friends house?

    Until we live in a perfectly digital world I believe there will still be a need for active recommendations.

    The problem with today’s passive recommender systems (I’m referring to MusicMobs and AudioScrobbler) is that, for most folks, they are totally worthless until you spend a month or two listening to all of your music on your iPod/iTunes. For most folks, it’s zero-effort => extremely slow gratification. Active recommenders are marginal effort => instant gratification. Have you played with MusicMobs/AudioScrobbler? You can’t get new recommendations until you’ve listened to at least ~50 more songs in iTunes. With GenieLab, you can sit there and go, “Well what if I rated Bob Marley a 3.5? Then what happens?” I think it’s a richer, more interactive experience.

    I think Amazon is a perfect example of passive recommendations gone totally wrong. I mean, it’s good today–they’re light years ahead of everyone else–but their recommendations are based on an extremely limited view of your tastes. They only recommend books based on books you’ve bought or browsed at Amazon. (Plus they only recommend books that they have in stock, but that’s another debate)… What about all the classics sitting on your bookshelf that you’ve read a half dozen times? What about all those great books your friends loan you? Amazon has no idea who you are, they’re just firing shots in the dark. I don’t believe their “long tail” success has anything to do with their recommender system, I think it has a lot more to do with just the size of their online collection. There’s no cleverness in what Amazon is doing, it’s just volume.

I’m wondering if our little email exchange served as motivation for a recent post on Chris’ blog, where he writes:

    …any service that tries to condense all of your different planes of influence into a single dimension is going to fail, at least as far as useful recommendations go. The filters that work best for me typically earned my trust by liking some of the same things I did, then turning me on to new stuff that I liked even more. 

Chris is referring here to “friendship” networks that provide recommendations, and he’s arguing that they don’t work. Yet the recommendations he trusts are the ones that he sought out: he invested the time to discover who was recommending what, and decide if their tastes coincided with his. Isn’t this a sort of “active” system?