4 ms·

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

by frossie 18y ago

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 18y ago
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.html http://anand.typepad.com/datawocky/2008/03/more-data-usual.h...
- metaguri 18y ago
  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.
- ntoshev 18y ago
  Content-based data may not help with Netflix prize, but they do help to quickly drill down to what people want to see. You can pick a "nano category" based on the meaning of the words in it, rating data can be superior but cannot be used interactively in such a manner. I've thought of doing a service like this movie search engine, maybe I still will. Search engines don't really leverage interactivity as much as they should. Consider a simple example: I choose Pi as a movie I like and Drama genre and get a search box to search within dramas similar to Pi. Providing context can go a long way towards getting what the user wants.
ilitirit 18y ago
> Well, it didn't work for me. Me neither, and "The Wrestler" was quite a glaring omission. I also agree that the "micro-genres" feature is flawed. For example, I searched for "Gwoemul" and none of the micro-genres it returned matched the reason I liked the movie - the deliciously dark humour. When I searched for Eight Legged Freaks though it returned "terrorize goofy carnage" which is a close enough, if over-simplified, description of Gwoemul. I suppose it boils down to the audience size as well. More people (on that site) are likely to have seen Eight Legged Freaks because it's older, and because it's Western movie (Gwoemul is Korean).
tyn 18y ago
Same thing here. I put Godfather II which is one my all time favorites and of course I got a suggestion for Godfather III which I consider a totally stupid movie. Apart from this I got suggestions for a bunch of gangster movies. I couldn't care less about the gangster theme, it's the quality of directing, acting and dialogue that makes Godfather II great. Ironically enough Godfather III is from the same director and with the same lead actor which supports even more the point that properties similarity is not a good predictive factor in movies recommendation.
- frossie 18y ago
  This is such a perfect example of what I was talking about that I shall thereafter think of this as the Godfather II problem :-) In a way the real triumph in this area is Pandora / Music Genome Project. It is interesting to think about whether and how such a thing could translate to movies - in other words a visual and verbal medium. I certainly don't think micro-genres is the right direction.
ConceptDog 18y ago
Ditto. I asked for movies like wargames, military, strategic, nuclear, and it was way off. It gave me movies like Meteor. I was expecting things like By The Dawns Early Light, Threads maybe, things like that. Sorry, complete miss.

by frossie 18y ago

henryl 18y ago

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

metaguri 18y ago
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.
ntoshev 18y ago
Content-based data may not help with Netflix prize, but they do help to quickly drill down to what people want to see. You can pick a "nano category" based on the meaning of the words in it, rating data can be superior but cannot be used interactively in such a manner. I've thought of doing a service like this movie search engine, maybe I still will. Search engines don't really leverage interactivity as much as they should. Consider a simple example: I choose Pi as a movie I like and Drama genre and get a search box to search within dramas similar to Pi. Providing context can go a long way towards getting what the user wants.

ilitirit 18y ago

> Well, it didn't work for me. Me neither, and "The Wrestler" was quite a glaring omission. I also agree that the "micro-genres" feature is flawed. For example, I searched for "Gwoemul" and none of the micro-genres it returned matched the reason I liked the movie - the deliciously dark humour. When I searched for Eight Legged Freaks though it returned "terrorize goofy carnage" which is a close enough, if over-simplified, description of Gwoemul. I suppose it boils down to the audience size as well. More people (on that site) are likely to have seen Eight Legged Freaks because it's older, and because it's Western movie (Gwoemul is Korean).

tyn 18y ago

Same thing here. I put Godfather II which is one my all time favorites and of course I got a suggestion for Godfather III which I consider a totally stupid movie. Apart from this I got suggestions for a bunch of gangster movies. I couldn't care less about the gangster theme, it's the quality of directing, acting and dialogue that makes Godfather II great. Ironically enough Godfather III is from the same director and with the same lead actor which supports even more the point that properties similarity is not a good predictive factor in movies recommendation.

frossie 18y ago
This is such a perfect example of what I was talking about that I shall thereafter think of this as the Godfather II problem :-) In a way the real triumph in this area is Pandora / Music Genome Project. It is interesting to think about whether and how such a thing could translate to movies - in other words a visual and verbal medium. I certainly don't think micro-genres is the right direction.

ConceptDog 18y ago

Ditto. I asked for movies like wargames, military, strategic, nuclear, and it was way off. It gave me movies like Meteor. I was expecting things like By The Dawns Early Light, Threads maybe, things like that. Sorry, complete miss.