I studied a lot of sociology in college and noticed a way that one could organize communities around their shared preferences of content so that people could discover new content with what should be a much higher degree of relevance then current recommendation systems.<p>I'm calling it a discovery engine, where the user can enter the name of a specific piece of content they have in mind, or something they are generally interested in, and receive recommendations of new content from like-minded people.<p>Just like Wikipedia issued a call to all people interested in making an encyclopedia, this would issue a call to all early adopters to be recognized as authorities and trend-setters. Think The Tipping Point by Malcolm Gladwell, but taking place online, efficiently, and transparently.<p>The project calls for combining social-bookmarking and user-generated media with an algorithm that both aggregates similar collections of content into networks and makes recommendations of content based on the evolving network structure. The ranks of "influence" and "in the know" are measured against networks of users with similar collections of content. These two rankings incentivize users to continually post relevant content because they want to remain "influential" and "in the know" in front of the people that are genuinely interested in the same content. The majority of users coming to Topiat for recommendations receive relevant recommendations fueled by the work of those who are genuinely influential and in the know.<p>But unlike current recommendation systems, which are domain specific and treat an individual as the sum of all their preferences (e.g. Netflix), the discovery engine would allow users to create networks based on all types of content (any combination of music, products, images, videos, URLs etc.) and enables users to explore different interests they have with the ability to create multiple groups of content on their profile. Each group of content becomes aligned with similar groups of content, from which recommendations are generated and delivered to the user (e.g. my oldies music compared with users with similar tastes in oldies music, my surfing group compared with other users that think of surfer the same way I do).<p>I'm putting together a Y combinator funding proposal based on this basic idea and am looking for feedback before I send it in. If there are any developers that like the idea and want to know more, let me know. Additionally, if you are good with machine learning techniques (e.g. neural networks) and are interested, let me know.
what's a one or two sentence explanation that would get my little sister to use this? what value would she get within the first 2 minutes (assume early on, before you have a ton of people using it?)<p>ideas like this are tough because they're only really useful once an incredible amount of content has been submitted. what tricks can you do to make the site be sticky to the first 100 users, when nothing yet is submitted? what are the first concrete things the user sees, or does, when they arrive at the site?<p>i think that's why these recommendation sites are only successful in niches (e.g. travel (tripadvisor), food/entertainment (yelp), movies (netflix)).
Here's an example of how I would use something like this:
I want to watch a movie that's similar to Jurassic Park. I can go to the site and type that in. The web application picks apart the constituent pieces of "jurassic park," like "action movie" and "dinosaurs" and "made a lot of money," then looks for other movies that match the criteria based on information from IMDB, Google, and MPAA. <p>Then, it looks for people who have voted/commented on the movie, and extrapolates a probability of you liking other movies that they have rated highly or poorly. If your rating history is similar to theirs, it rates their other movies with similar pieces higher. If your history is very different, or if they voted Jurassic Park very poorly, then the other items they voted highly are placed low on your recommendations.<p>I think that's awesome. <p>
Description is too long, doesn't make sense. However it also sounds like the plan of no fewer than 10 already existing startups with tons of venture money that are already doomed to failure ... Except for Ning.
This is kind of what I tried to do many months ago. It was also based on the premise of transparent authority, many-to-many content and user connections, categorization on multiple dimensions, and such. I also approached it from a sociolinguistics angle. The problem with sociology and the like is that the metrics themselves are very broad and hard to quantify.<p>The application's complexity spiraled out of my control. I still believe in the core concepts, but execution of this thing in particular is very tricky. Theoretically speaking it could be the end-all recommendation engine, and that is, from my own experience, almost as lofty as building a Turing Machine. Granted that you have user input, it probably only needs to be half as strong, but that is already very strong, and very difficult (at least for me).<p>Now logic is one thing. One the same level of difficulty is how to make it user friendly; for this app, regardless of how hard the AI is, the UI is probably half the battle.<p>I'm not sure what kind of app you have in mind specificially. dcurtis's mockup is real slick and to the point. My implementation was a lot more passive, but I suspect internally the structures share commonalities with your proposal. If you are interested we can discuss. I won't be applying with my implementation though, because this the kind of site where without 100k users, a VC won't even bother. And no matter how good it is, it is much harder to get traction today, hence higher uncertainty, higher burn.
Sounds like IRC. Once you find a chan you like and kind of settle in, you realize that the actual convo rarely relates to the title of the chan, but is still of interest to the people who regularly go there.<p>IRC is pretty popular, I don't see why your idea wouldn't be among the less BitchX-inclined internet crowd. ;-P<p>You'd have to be <i>brutal</i> with the 'influence' system, though, to keep the trolls out of the 'ponies and barbie dolls' group.<p>The killer feature, to me, of an idea like this would be something like musicovery.com, where overlapping things lead you down new paths.
<i>"... I'm putting together a Y combinator funding proposal based on this basic idea and am looking for feedback ..."</i><p>demonstrate the idea by showing ... build an app... Show me the demo
The idea seems to be, basically, make a better social filtering application. I definitely think there's space for something really cool here, but no one's done anything close to useful yet, short of Amazon. <p>If I were you, I would try to, at least for the time being, try to pare the idea down into something much smaller and more manageable. Once you have that done as a proof of concept, if it's still too small you can expand to other areas. Along the way, you will've learned a lot about what you'll need to execute on the bigger idea. Why not start with the movie suggester you mentioned later on in the thread?
What determines "groups of content" (people? algorithms? either way, the value is in figuring out how)<p>It isn't clear why "domain-specific" recommendation systems don't work within a group.<p>Note that it isn't obviously true that separating by group actually produces better recommendations. In fact, the (inadequate) evidence that I'm aware of indicates the exact opposite.<p>BTW - If you don't have the algorithms mentioned, how much do you think that you actually have? (I can imagine lots of wonderful things that would come from a personal transportation device that got 100mpg, but if I don't know how to make one....)
Delicious allows users to put a permissive license on their RSS feeds, so (IANAL and within reason) you can probably get those bookmarks and importantly tags. So how closely would making recommendations per tag match your idea? <p>Tags are a little messy, and perhaps overly specific, so you may need to cluster them and then do recommendations on that. <p>I'm not sure I understand how you plan on making 'influence' work, if someone bookmarks a link recommended to them do you simply increment all those who have bookmarked it before? <p>Please don't use the word incentivize, its terrible :)
utter crap. Go back at the drawing board. Make something more tangible, enough with the pies on the sky.<p>These are concrete ideas:<p>
Facebook, just like your silly school book, but online, and you can add friends<p>Google - search engine<p>Yelp - reviews of restaurants and businesses<p>las.fm - music recommandations from your friends<p>pandora - discover music according to your tastes<p>reddit/digg/news.yc -- recommending news and stories<p><p>Yours is not a tangible idea. Narrow it down to one sentence the main idea, and just one paragrapsh on how it will work, and then you have something more tangible.
I'm not sure if it's possible to get enough traction for a general purpose recommendation system. Could this be a service that you offer to providers of niche sites? In terms of machine learning, I think you need to look at algorithms that work well with sparse data. This is particularly important in the beginning, but even later you might not have much data in every single field of interest. Good luck.
its called digg.com and about a dozen other sites I've seen. You could even build most of this in Ning for free, right now.<p>My point being that I don't see anything in there that really evolves the social content sharing platform in any significant way.
sounds like it can be taken care of by mahalo. machine learning < human sorting. i'm also not seeing a business model.<p>or... i might have said that because i want to discourage you from using this great idea and save it for myself... hmm...