4 ms·

"groups of content" are collections of content that users create themselves (the social bookmarking aspect of the site). Users that want to build a reputation

by steffon 19y ago

"groups of content" are collections of content that users create themselves (the social bookmarking aspect of the site). Users that want to build a reputation for being in the know and influential are incentivized to make these collections relevant. The groups also become lists users can form to bookmark content they find around the web and want to save it one spot. Based on what you have put into your collection of content, and collections others have made, the algorithm aggregates those similar people into the same network. People in the same network get content recommended to each other from their like-minded peers that they have not discovered on their own. With regards to the feasability of such an algorithm, I've talked about it with many mathematicians and machine learning programmers and the wheel does not have to be reinvented for this application. The tools already exist, and just have to be customized and tweaked for this application.

anamax 19y ago
In other words, I make a "group of content" that I call "movies" and it gets compared with other peoples' "group of content" that they called "movies". Why isn't the netflix recommendation system useful for generating recommendations from "groups of content" labelled "movies"? If it works for movies, why can't it combine "groups of content" labelled "science fiction"?
- dcurtis 19y ago
  The point of having groups of content is to combine traditionally dissimilar types of content-- movies, music, housewares, etc... For example I can make a group of content called "new living room" and add all of the things that go into my new living room. This includes the music and movies that I have stored there, the type of TV I bought, the type of couch, stereo receiver, speakers, or even the paint on the wall. When someone searches for something within that collection, the system knows that someone else, somewhere, has combined that "thing" with the other "things" in the collection, so they get rated higher as being compatible.
  - anamax 19y ago
    Why do you think that the netflix system won't do the right thing if we both put some music in our "groups of content" labelled "movies"?
    - steffon 19y ago
      This page explains many of netflix's limitations well: http://harry.hchen1.com/2006/10/03/391 http://harry.hchen1.com/2006/10/03/391. But more importantly, look at these limitations in light of how the discovery engine is organizing its preference data and how it's collecting preference data. The critical difference with the discovery engine is the idea of a group of content that users fill themselves with content based on criteria they see as relevant. Yes, a users aggregate preference composition is important, but what is more important is their set of preferences regarding a specific collection of content. This way, a user can be really into classical music, horror movies, and modern furniture, and get relevant recommendations for each interest, connecting with people who are most in the know regarding each interest.
    - dcurtis 19y ago
      I'm not sure what you're asking here-- Netflix doesn't care about anything but movies, and it probably wouldn't be able to recommend movies any better if it knew your musical tastes, or even how your tastes compare to mine. The idea is that if you search for "sony SSK70ED," on the "discovery engine," it will show you what other people have paired with those speakers, such as receivers, furniture, and televisions. In a way those things are "similar" to the speakers because they complement them. Of course, the system shows you similar speakers first, but the complementing items are interesting results to have when you're searching for a specific item.
      - anamax 19y ago
        > I'm not sure what you're asking here-- Netflix doesn't care about anything but movies, and it probably wouldn't be able to recommend movies any better if it knew your musical tastes, or even how your tastes compare to mine. That's wrong. The only thing that is movie-specific about the netflix recommendation system is the preference data that it runs over. It doesn't know movies from eyebrows. If netflix (the company) also collected preference information about music, the recommendation engine would predict music preferences. And, since it would have both music and movie info, it would use music prefs to recommend movies and the reverse, just as it uses movie prefs to predict movie prefs today. Amazon's "users who bought {something} also bought" is an example of "doesn't know anything about the domain". (They have to tone it down to keep it from recommending "strange" things that are way out of category.) Disclosure: I know the guy who implemented NetFlix first recommendation system and have written a collaborative filter myself. I know what I can do with the fact that we both like the Pogues and Chunky Monkey. I still don't see what I can do with how we group those preferences.

by steffon 19y ago

anamax 19y ago

In other words, I make a "group of content" that I call "movies" and it gets compared with other peoples' "group of content" that they called "movies". Why isn't the netflix recommendation system useful for generating recommendations from "groups of content" labelled "movies"? If it works for movies, why can't it combine "groups of content" labelled "science fiction"?

dcurtis 19y ago
The point of having groups of content is to combine traditionally dissimilar types of content-- movies, music, housewares, etc... For example I can make a group of content called "new living room" and add all of the things that go into my new living room. This includes the music and movies that I have stored there, the type of TV I bought, the type of couch, stereo receiver, speakers, or even the paint on the wall. When someone searches for something within that collection, the system knows that someone else, somewhere, has combined that "thing" with the other "things" in the collection, so they get rated higher as being compatible.
- anamax 19y ago
  Why do you think that the netflix system won't do the right thing if we both put some music in our "groups of content" labelled "movies"?
  - steffon 19y ago
    This page explains many of netflix's limitations well: http://harry.hchen1.com/2006/10/03/391 http://harry.hchen1.com/2006/10/03/391. But more importantly, look at these limitations in light of how the discovery engine is organizing its preference data and how it's collecting preference data. The critical difference with the discovery engine is the idea of a group of content that users fill themselves with content based on criteria they see as relevant. Yes, a users aggregate preference composition is important, but what is more important is their set of preferences regarding a specific collection of content. This way, a user can be really into classical music, horror movies, and modern furniture, and get relevant recommendations for each interest, connecting with people who are most in the know regarding each interest.
  - dcurtis 19y ago
    I'm not sure what you're asking here-- Netflix doesn't care about anything but movies, and it probably wouldn't be able to recommend movies any better if it knew your musical tastes, or even how your tastes compare to mine. The idea is that if you search for "sony SSK70ED," on the "discovery engine," it will show you what other people have paired with those speakers, such as receivers, furniture, and televisions. In a way those things are "similar" to the speakers because they complement them. Of course, the system shows you similar speakers first, but the complementing items are interesting results to have when you're searching for a specific item.
    - anamax 19y ago
      > I'm not sure what you're asking here-- Netflix doesn't care about anything but movies, and it probably wouldn't be able to recommend movies any better if it knew your musical tastes, or even how your tastes compare to mine. That's wrong. The only thing that is movie-specific about the netflix recommendation system is the preference data that it runs over. It doesn't know movies from eyebrows. If netflix (the company) also collected preference information about music, the recommendation engine would predict music preferences. And, since it would have both music and movie info, it would use music prefs to recommend movies and the reverse, just as it uses movie prefs to predict movie prefs today. Amazon's "users who bought {something} also bought" is an example of "doesn't know anything about the domain". (They have to tone it down to keep it from recommending "strange" things that are way out of category.) Disclosure: I know the guy who implemented NetFlix first recommendation system and have written a collaborative filter myself. I know what I can do with the fact that we both like the Pogues and Chunky Monkey. I still don't see what I can do with how we group those preferences.