Slashdot

If no one wins within a year, Netflix will award $50,000 to whoever makes the most progress above a 1 percent improvement, and will award the same amount each year until someone wins the grand prize.

But if someone does win within a year they will still have the ability to use others' code, free of charge, as part of their product.

The article doesn't say but how will you know if your code is making choices better than their existing system? I wouldn't be submitting my code unless I was sure I was going to win. Then again I'm not a gambler or a coder ;)

Says one researcher: "You're competing with 15 years of really smart people banging away at the problem."

So, the professionals have been working at it for a long time. Is it safe to assume some teenage to early college hacker will find a success within two weeks.

if(user.getGender()==Person.MALE)
recomendation=MovieGenre.PORN;
else
recomendation=MovieGenre.CHICKFLICK;

And of course, slashdot must have sensed my post as my image word is "pervert"

..except, instead of making it open to the community (which is not a bad idea, I must say) I thought of having Google do it. This is, perhaps, IMHO, a much better idea. Now, what we really need is a Movie Genome Project, much like the Music Genome project that lead to Pandora.

They have decent tech for building similar/recommended alternative pages.
Especially the newer blogish type pages where theres a gallery and a small selection underneath.

Not that I would know of course.

As a NetFlix user I have one suggestion for their recommendation system that can make it much better. Make it aware of the connection between series. That is to say, If you rent season 1 of something, suggest season 2, not season 4 (even if season 4 has better review ratings). If I mark season 1 of something as "not interested" instead of giving it a user rating, don't suggest every other season of that same show at the top of my recommendations. I mean how many times do I have to tell you I don't want to see any season of "Friends" ever, even if you pay me?

How will they handle privacy issues? Don't the same issues appear here that appeared with the AOL data this summer? With enough ratings you can narrow down to a specific person, and then find out about all the pr0n that this person has been getting as well.

In a quest to better movie recommendations, Netflix is opening their database (nytimes, registration and first child required)...

Not quite, you can find it here [nytimes.com] (or the minimalist version [nytimes.com] for anyone sick of ads).

Why is it that the Slashdot editors are just too damn lazy to look up the RSS feed links to these pages?

The problem is not easy. Says one researcher: "You're competing with 15 years of really smart people banging away at the problem."

While this may be true, I wouldn't let it deter you. Collaborative filtering is a field that is far from dead. The interesting thing about collaborative filtering is that on the surface, it seems pretty straight forward but once you dig into the mechanics of it, there is actually a lot of playing you can do. Ironically, the way you display the data to the end user is often what determines how well of a job you did.

Allow me to take a naïve approach at this topic and say we generate a movie index of each person. I would have A Clockwork Orange and Koyaanisqatsi at 5 while The Ring 2 would be at the very low end. My friend might have similar movies. If he has A Clockwork Orange up there, you might be able to compute a Euclidean distance between us. However, this approach falls apart because no one has seen Koyaanisqatsi and of the 20 movies I've ranked highly, they are hard to find.

You don't have to stop there, however. You could also database the movies I marked as "uninterested" or the movies that were presented to me but I didn't vote on. Like if I had seen the offer to mark J-Lo's latest flop but didn't, wouldn't that tell you something about me?

So these caveats present themselves all along the way and, at the end computation, you have many different strategies for this data. For example, while you might not be able to link my friend an I through movies, how far apart are we on a nod network? What I mean is, if you plotted every user in their own dimension depending on the movies they ranked and attempted to compute as good a distance as possible between all users, how far would I be away from my friend by hopping on these nodes? There's a lot of information to be gleaned in this sort of friend-of-a-friend collaborative approach.

Now you need to present this information to the user. Do you just up and recommend him a movie? Do you take Amazon's approach and say "Other people did this -- so should you."? Or do you give them some sort of three dimensional flash plotting of you versus the people nearest to you? Do you allow the user to contact those closest to them? Those farthest away?

My point is that while 15 years of research has been done, it doesn't mean there's been 15 years of testing and implementation which, in the end of creating products, is where most of the importance lies.

The problem with recommendation systems is that they use too little information to catagorize their subject.

What they need to do is copy the methods of the Music Genome Project (www.pandora.com), and list a larger set of attributes for the films. This way it can recommend films by checking many more characteristics, such as director, tone, writer, or subject.

If you can beat "15 years of really smart people", then your work product probably has more than a million dollars in value if you were to license it out to places like Amazon, eBay, Netflix, etc. Even a 1% improvement in revenues from a 1% improvement in recommendation accuracy is probably worth more than 50K, if sold to the major e-tailers. On the other hand, if you just want an interesting problem to screw around with in your spare time and don't want to go through the bother of forming a company in order to monetize that work, this is a pretty cool opportunity.

I wish they'd fix the problems in the logic determining what they actually send me from my queue before fixing problems with what they recommend to me. If I've got season 1 of a show in my queue prior to season 2, don't start sending me season 2 because some disc of season 1 is unavailable (which has happened to me multiple with both netflix and blockbuster online), send me something else completely. They've got the tech to keep one season of a tv show in order, it can't possibly be that difficult to extend that to keeping multiple seasons of a show in order.

On top of that, don't show me that it's available in my queue but send me something else instead. While I haven't asked netflix about this, I have asked blockbuster online, and I imagine they are both doing the same thing. The disc is "available" just not at the warehouse used to ship to me personally. Instead of basing one piece of information off of total stock and one off of local stock, base them both on the stock at the warehouse shipping to me.

If Netflix doesn't have the movie in stock it should burn the movie on demand.

Any marketer will tell you that what people tell you they want and what people actually want are very different things. Even if people answer honestly, the data you gather is often unreliable: people simply don't have as good a handle on what they want as they think they do.

Not that marketers have a better handle, but simply that people will swear up and down that they would buy a peanut-butter-filled hot dog, that they loved the one they tried, and then don't actually buy any.

Don't believe me? Go see Snakes on a Plane. Nobody else did. (Sure, $33 million seems like a lot, but that's chump change for a major studio release these days.)

The best improvements will come from insights gained between the lines. You may have rated The English Patient eleventeen stars, but if your next seven rentals were all episodes of The Girls Next Door, which you only rated 3 stars, it certainly looks like you want more Hugh Hefner and less Ralph Fiennes.

The best data is the data that the subject doesn't realize he's giving you. Once you start imposing conscious choice on the ratings, you get only what they say they like, not what they really like.

I stopped rating movies after I found that I got recommended a lot of crap. Say I rent a slasher movie that, for its genre, is artfully done. I rate it high. Now I have recommendations for a bunch of worthless, straight-to-video stuff that I really don't want to see.

This is the real nut to crack, IMO. How do come up with an algorithm that rates 'quality,' an elusive concept that means different things to different people?

Not to mention, I'm fickle.

I personally weigh movies on a number of different factors. I might give 3 stars to a movie because it has 4 of my favorite actors in it even if I didn't care for the plot. I might give 3 stars to a different movie with horrible acting but interesting camera angles (From Dusk Til Dawn 2). I tend to average out my ratings dependent on many things a movie has to offer.

The problem is is that that is my rating system. It works for me. But it does little good to anybody else because they are rating based purely on something else.

I think they need to implement the ability to rate more aspects of the movie. I'm sure some people out there rate the movie poorly if their disc is scratched or the transfer quality is poor even. A simple 1 to 5 system doesn't cut it. People rate things that aren't "Was the (romance) plot good?", "Do you like this director?", "Do you like these actors?". People rate things that aren't on the box.

Disclaimer: I subscribe to the same sort of service, except through blockbuster... maybe Netflix does have this feature. My wife and I share a queue... I imagine many, many of these queues are shared. We have very, very different tastes in movies. Instead of getting recommendations that suit us both (which is next to impossible), the recommendations just get very, very confused. If I could just keep my and her recommendations from tangling, we would both have an easier time.

I see that the NYT article linked to just about everything except MovieLens [umn.edu]. I've used the site, and folks might like to try it out. It looks simple, but it's fairly nice, having some of those fun dynamic pages that are all the rage these days. One neat thing in comparison to Netflix is that it will give a projected star rating for you, rather than simply saying "Recommended".

Of course, I'm biased since I had John Riedl as a professor in a few easy classes. I think he tried to spin off this research as a new company, but I'm not sure if it ever got off the ground.

One thing I'd really like to see has little to do with the quality of ratings, though. I'd like to be able to keep a common database of my ratings across multiple sites. At the moment, I've rated a number of movies at Netflix, MovieLens, and IMDb, but they aren't entirely consistent. Unfortunately, two of the sites use a ten-point system (IMDb has a ten-point scale, MovieLens goes up to 5 stars, but in half-star increments), while the other uses a five-point one (maybe six if you say "Not Interested"..).

Well, I'll have to poke around a bit with this stuff. I wouldn't be able to do much, though, since my level of knowledge in this arena is very limited...