
A movie recommendation service that actually works - noaharc
http://nanocrowd.com/
======
frossie
Well, it didn't work for me. If it is any consolation, no recommendation
service usually does.

Here is the problem: recommendation engines usually try to match common
variables between the movie you like and other movies - kudos to nanocrowd for
doing this at a more sophisticated level than "it has the same actor in it" -
but most fail to weigh heavily enough the quality of the movie (Netflix is
notorious bad at this). More to the point, the movies that people really love
have some personal connection with them that I am not sure is open to
crowdsourcing.

I will now give you an example: I love the movie Pitch Black. Why? Yes,
there's the action/horror tension, and the fabulous spaceship crash, and the
charismatic lead - but the reason I _love_ that movie is because of its kernel
which is a highly moral tale about the salvation of caring for someone other
than yourself, and of the powerful human need to seek redemption.

Now I am not going to reproduce this here, but go put in Pitch Black in
nanocrowd and you will see the problem: it focuses on the superficial
properties of the plot, and none of the movies recommended come even close.

What is the closest movie in feel that I have seen? The Station Agent. Now the
day I type in "Pitch Black" in a recommendation and get back "The Station
Agent" is the day I am going to sell all my wordly goods to buy stock in that
company.

(That said it is safe to say most people aren't as picky as me, and I am sure
you can find some success with this model especially if you harness it to
something people visit a lot anyway, like IMDB or Netflix).

~~~
henryl
I find Netflix's recommendations to be far superior. Participants in the
Netflix Prize have found that content-based filtering (such as "nano
categories") actually hurts relevancy (in RMSE terms) when working with large
data sets. That is, when you have a copious amount of data, it is usually
better to let the data speak for itself. Similarly, it has been noted by
Google and others that the better your data, the less impressive your
algorithms need to be. Most people think Google's success is mainly attributed
to magic algorithms, but most of their effort of late has been in increasing
the quality and breadth of their data.

[http://anand.typepad.com/datawocky/2008/03/more-data-
usual.h...](http://anand.typepad.com/datawocky/2008/03/more-data-usual.html)

~~~
metaguri
I was under the impression that Netflix looked at your ratings, found people
"similar" to you, and made recommendations based on that. Since signing up for
Netflix, I"ve found that at least on the UI side, it's as you describe: driven
by actor, genre, etc.

The beauty about the raw data approach is that it finds people with similar
preferences to you. This is, I think, the real manifestation of O'Reilly's
"Web 2.0." Rather than a semantic web, with ontologies and categorization the
data do, indeed, speak for themselves.

Even if we were able to extract categorizations based on the preferences in
the underlying Netflix data, it'd be difficult to map them to actual
categories that we're familiar with. I'd envision it more like a PCA
decomposition, where the principle components will be the strongest
characteristics among each clique of like-minded movie watchers.

But alas, my Netflix home page is filled with crappy movies (the Matrix has a
near-5 rating, but most other movies with its actors are complete crap).
Instead I rely on friends with similar preferences for recommendations...
which is what Netflix was supposed to offer. If that functionality is hidden
somewhere, then they need to do a better job of exposing it.

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rsheridan6
The autocomplete is nice. The selection is lacking. There seem to be virtually
no foreign films, for example. The most important implication of this is that
I've already seen everything it recommends (but that at least means that the
recommendation algorithm is on target).

Making the user click on a nanogenre after entering a movie is unnecessary -
you could show at least a partial list of all of them instead (and maybe show
more of a particular list if you click on it).

Overall, I like clerkdogs better, mainly due to the wider selection.

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knightinblue
The problem I see is that it _forces_ the user to pick a 'nanogenre', a set of
3 arbitrary characteristics - what if I'm looking for a mix of charateristics
from within the different nanogenres? It won't let me choose the exact mix of
characteristics I want and instead, restricts me to the ones it displays.

I understand that recommendation engines need to work within certain
predefined parameters, but that's exactly why they'll usually disappoint - you
can't categorise a person's preferences into predefined parameters. Most of
the time, there's no real reason _why_ someone likes a movie and hates a
logically related movie.

Personally, I prefer clerkdogs.com

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kqr2
I wonder if this suffers from the _Napoleon Dynamite Problem_ :

[http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?...](http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?partner=permalink)

~~~
jcl
Curious... From the end of the article:

 _Hastings is even considering hiring cinephiles to watch all 100,000 movies
in the Netflix library and write up, by hand, pages of adjectives describing
each movie, a cloud of tags that would offer a subjective view of what makes
films similar or dissimilar. It might imbue Cinematch with more unpredictable,
humanlike intelligence._

This appears to describe what the linked search engine is doing.

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geuis
I searched on some of my favorite movies and was very impressed with the
accuracy of the results. I've seen 95% of the movies it recommended, but I
loved almost all of the ones it did. I think this would be good for finding
movies that I would like to watch that are outside of my normal genres.

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pedalpete
The site looks great, and your auto-complete is amazing! How can IMDB not have
that?

However, i entered Sweeny Todd. I get a mostly blank page and am asked to pick
a sub-genre. None of which really fit what i'm looking for (dark & musical).
so I try the sub-genre thing, and it just isn't working. But then I see the
left column with "movies most like". I'm assuming that is the main feature of
the site. So why on earth do you not put that front and center, and if I want
sub-genre, I can do that after??

Aside from that, i think the service is pretty good.

~~~
ken
Strange. My first reaction is that the auto-complete was so bad that it,
alone, would keep me from ever returning. I typed in "Ran" (the last film I
saw), and then I had to click a tiny down-arrow about 25 times, and the down-
arrow jumps around as you go. What takes "3 letters + return + click" on IMDB,
took "3 letters + 25 precise clicks + return" on Nanocrowd. Autocomplete is
usually an optional assist; here, it's more like a mandatory in-place search
system with awkward controls.

I would have left a note about this (and other issues), but their only
feedback mechanisms seem to be email or logging in to Blogger.

It may be the coolest recommendation algorithm ever, but from these first two
things I tried, the interface seems fairly high-overhead. You need to hook me
before I'll go for high-overhead. You need to convince me that you're more
valuable than, say, simply listing other films by the same director. For
"Ran", Nanocrowd recommends "Rambo" -- 'nough said. :-)

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sachinag
I guess it's Pandora's HGP for movies, which is cute, but presenting me with
buckets doesn't really work.

<http://nanocrowd.com/genre/nanogenre/id/3629> \- The Iron Giant is all of
these things, not just one.

~~~
noaharc
Well I think that's the idea. It lets you choose related movies based on
specific aspects of the seed, so that what it returns is as well-tailored to
your appetite as possible.

Just giving a seed is much more difficult (see: Netflix Prize).

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chime
Wow! I think it indeed does. I clicked on the link thinking "sure... another
recommendation engine" but I was surprised at the effectiveness of the method
they use to make the suggestions: "3-word nanogenre." I searched for one of my
favorite movies 'MirrorMask', clicked on 'fantasy, wondrous, surreal'
nanogenre, and I got a list of films, many of which I loved:

[http://nanocrowd.com/movie/genremovies/genreId/1814/movieId/...](http://nanocrowd.com/movie/genremovies/genreId/1814/movieId/7057)

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bpm140
There really hasn't been a good movie recommendation engine since LikeMinds
got sold to IBM and their MovieCritic.com site was shuttered.

One of the original brains behind their collaborative filtering technology
launched a similar site a few years ago at moviepig.com, but sadly the entire
thing is done in flash and the design is so awful that it eclipses the fact
that it makes pretty solid movie recommendations after you rank order a couple
dozen movies. Worth a try.

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davidmathers
Hmm. Amadeus is a "lavish music musical" and a "lavish historic historical".

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krav
Nicely done.

