If You Liked This Post, Perhaps You'd Like To Look At The History Of Failed Recommendation Systems
from the we-recommend-you-read-this-article dept
For years, we’ve been hearing about the holy grail of the perfect recommendation system — and yet, every time these systems fail to live up to the hype. Early on in the web days, there was FireFly and its amazingly overhyped collaborative filtering system that was supposed to revolutionize the space. Others have come and gone, and while the systems have improved marginally, they’re still far from useful on a regular basis. They’re either exceptionally limited, or they tend to provide mostly obvious solutions. That is, of course, if they can provide decent recommendations at all. There was the famous article a few years ago in the Wall Street Journal about how people were noticing when their TiVo’s recommendation system thought they were gay after they recorded a “gay” show. It’s gotten so bad lately that Netflix has resorted to throwing up its arms and asking programmers everywhere for help.
That’s why it’s a little surprising to see Fortune Magazine claim that these types of personal recommendation systems are somehow the next big thing. It’s true that they’ve always been something of a holy grail for marketing types, who would love to be able to provide more accurate and relevant choices to you at all times. However, we’re still a long way away from that. While some of the hype over personalization in the ad space got swept aside when Google figured out how to do better keyword matching and contextual advertising, many now believe that this kind of “discovery” advertising — recommendations based on what you’re like, rather than what you like or what you just searched for — is the future. The problem is that it’s not an easy problem to solve, and the long history that’s littered with failures in the space would suggest that just claiming you’ve got the best solution (or performing simple parlor tricks, as one group does in the article) isn’t actually enough to signal a revolution.
Comments on “If You Liked This Post, Perhaps You'd Like To Look At The History Of Failed Recommendation Systems”
I loved this article!
thanks for the reco!
all kidding aside, doesn’t it boil down to constantly changing option sets? as long as variables keep changing, it’s hard to predict how groups and subgroups and individuals will respond to new choices as they become available. a truly difficult problem!
When Amazon gets it right I’ll know things are turning for the better, but I don’t see that coming for quite a while. I’ve purchased over one hundred art books, have thirty in my wish lists, I rate books I look at and/or buy, I say which ones I’m interested in and which I’m not Still, Amazon can’t make a recommendation worth the price of the bandwidth it took to send the page.
Amazon makes great recommendations for me, it’s just a little slow. By the time they recommend a book, I generally already have it. 🙂
Im not advertising here and anyway this site is free. But if u want to see some reccomendation in action just checkout pandora.com. Sometimes itll work perfect for me, other times it starts throwing stuff at me I would never want to listen to.
I really don't want any recommendations
If I want something I shop for it and buy it. The chances that has anything to do with what I will buy next are pretty slim. I may need a new monitor, do my research and find one that crosses the line of cost / benefit and buy that one. The next thing I may need is a backhoe muffler. Exactly how is any program going to make any sense of that. I welcome recommendations just as much as I do telemarketing calls.
Why would I believe Wall Street predictions about the “next big thing”?
Wall street only has one goal – to make you buy and sell and repeat that cycle often. Therefore, they have no credibilty.
Any profits or losses you experience by following their recommendations are of no interest to them.
Ooooh – it must be the next big thing – Wall Street says so. NOT!
failed recommedation systems
I read a thing on recommendation theory, the other day, as relates to advertising and what we’re going to have to deal with in the future.
TiVo and TV on Demand are going to change all this just as soon as they figure out how to get cameras into their equipment (that digital box over top of your tv) and sell us on the idea that being constantly watched is a good thing.
Allegedly, they’re going to watch (and guage) our reactions to commercials and then decide what commercials to send, based on our reactions. Did we get up and walk away from that truck commercial? Are we watching, in rapt attention, to that sexy lingerie commercial?
And what are our children doing?
What does it take to keep our attention? (And whose attention do they want to keep?)
You don’t need a person to decide this, the technology is available for computers to do this.
Conspiracy theory or just a matter of time?
It isn’t new by any means but the systems are geting smarter for sure. Netflix derives a good portion of thier revenue from thier recommendation system so much so that other DVD rental services have added recommendations to thier sites. Netflix is even ponying up 1mill to the person or group that can increase the recommendations systems acurracy by 10 percent (not easy).
As computers get faster and there are more datapoints to collect these systems will become better.
False Negatives, False Positives
I loved “Long Kiss Goodnight” and I loved “Kiss Kiss Bang Bang”, but I will never rent or buy a “Lethal Weapon” movie, despite the fact the same script writer is responsible for them all.
Why not? Because I hate Mel Gibson, because he’s a racist, sexist, homphobic bigot and holocaust denier.
How is a recommendation system supposed to figure that out? A vector in n-dimensional space with a dimension just for Mel?
I personally think that music recommendation systems such as http://www.last.fm are the most effective.
Last.fm logs the music you are listening to and uses this to build a personal history/profile. This is then compared to thousands of other profiles to see which people have the most similar listening habits to your own (your ‘neighbours’). Music that these people like that you have not yet listened to is then recommended to you (and it works very well!).
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