7 ms·

Directed Edge sees a post-search web where recommendations rule

108 points by GVRV 17y ago

pclark 17y ago
Hacker News user "Wheels" is a founder - congrats on the write up. Didn't realize you were YC funded
davidw 17y ago
Wow, congrats to wheels & co for the TC hit and YC - I don't think anyone knew about that!
rwolf 17y ago
"We can take data sets with millions and millions of data points and figure out what’s related to a given item in a few milliseconds. Most recommendations engines pre-compute stuff rather than generating the recommendations in real-time like we do" I've been looking into recommendation algorithms recently (started with the excellent book "Programming Collective Intelligence"), and this sounds lightyears ahead of the way we currently do things. I suppose you could take one of the algorithms that requires pre-computing and throw resources at it, but it seems like they are talking about something new. Since I'm just getting started, I'd like to find some academic (or blog-faux-academic) articles on whatever recent advances behind recommendations without the need for precomputing. Anyone know where to look?
- 10ren 17y ago
  They have a couple of articles on their site: http://directededge.com/tech.html http://directededge.com/tech.html
  - rwolf 17y ago
    Thanks, you replied right before I was going to edit in the answer from http://developer.directededge.com/article/Introduction_to_Recommendations http://developer.directededge.com/article/Introduction_to_Re...: "Graph-based recommendation systems". Something new to learn! edit: the linked page mentions the book I'm reading! Looks like the answer was a few pages further in :)
- ntoshev 17y ago
  There is no magic. I don't know what Directed Edge are doing, but simpler Amazon-style recommendations (people who bought X also bought...) doesn't need to precompute anything if you choose your data structures properly: get a list of things people bought along with X, count them (or actually store them counted), optionally normalize for general popularity, sort in decreasing order, show top results. I'd love to hear something from wheels though.
  - paraschopra 17y ago
    So, you presume doing all this does not take any time for millions of objects?
    - deleted 17y ago
      [deleted]
    - ntoshev 17y ago
      All I'm saying is that it's possible with the right data structures. It's not more than what a typical search engine does.
  - wheels 17y ago
    Amazon's algorithms are simpler than our own (at least as far as I can tell) and most recommendation engines use some sort of embedding to reduce the dimensionality of the problem. Amazon's related products do in fact seem to be, or very near to, a simple counting structure. Our ranking algorithm builds a large subgraph around an item and then does a few passes with a couple different ranking schemes to try to figure out the hot items within that subgraph, prunes "noisy" connections (i.e. links that are "hot", but don't actually pack much semantic meaning) and then tries to scale things so that the results returned aren't simply those with the largest overlap, but those that are most relevant within that subgraph relative to the larger graph. In that sense, it has some similarities to web-search algorithms. In user-visible terms, that means that our results are often less obvious than Amazon's recommendations -- for a long time we called that the "tell me something I don't know" problem. It's no good to do a search for "Miles Davis" and have "Jazz" com back as a related item. If you know about Miles Davis, you already know about Jazz.
bandris 17y ago
Also congrats for DirectedEdge for doing the animation without flash on the front page. Nice!
- wheels 17y ago
  You can actually search in the little bar there for any wikipedia article to show related stuff, and as kind of a little easter egg, you can specify a starting page via the query string, e.g. http://www.directededge.com/?Y%20Combinator http://www.directededge.com/?Y%20Combinator
  - djuedemann 17y ago
    This little wikipedia search bar is hiding nodes from the graph most of the time. It bothers me a little.
    - wheels 17y ago
      We tried a few combinations -- and may try some yet. If it was further right it threw off the balance of the layout. If it was up at the top it drew too much attention to itself. We tried messing with the z-index on hover too, but that looked funky.
      - djuedemann 17y ago
        I thought it would be nice if the user could drag it around.
- leif 17y ago
  Haha Paul was disconnected from Jessica for a bit. A few more clicks and they were back together. Awww.
utnick 17y ago
cool, have you thought about entering it in any online recommendation contests? ( like netflix or the github one )
mattmaroon 17y ago
"Directed Edge truly believes that we’re about to see a shift on the web away from search and towards recommendations." The difference is somewhat arbitrary when you think about it. When I Google, I'm asking it to recommend me stuff related to what I'm looking for. Google is nothing but the world's best recommendation engine. There are about 1,000 sites that could use good recommendation technology to enhance their profits though, so I like this company's monetization chances. Easy elevator pitch too: recommendations as a service.
- ntoshev 17y ago
  You are right, there is a continuum of possibilities between pure search and pure recommendation systems. The difference is in the input data that the search/recommendation engine considers: the input in classic search is only the few words in the query, while a recommendation engine considers some history of your interactions with a site or the internet (and nothing that indicates your intent in this particular instance). Google, of course, keeps search history for people logged in. But it doesn't seem to affect their relevancy algorithm much, unlike, say, your location and language. I'd speculate Google Checkout is mostly about gathering data that would allow for meaningful recommendations.
- etherealG 17y ago
  I was about to post this too. Surely all the possible nodes include all possible search terms, as well as all web pages. Then search and relations are synonymous. Any thoughts on how your system would be different from a search engine in that sense Wheels?
- wheels 17y ago
  It's a continuum, as hinted below, but the results are pretty different in the polar cases. I'll fall back to my Miles Davis example. Searching for "Miles Davis" returns 10 pages about Miles Davis. Hitting our engine with Wikipedia data for Miles Davis gives you John Coltrane, Herbie Hancock, Wayne Shorter, Thelonious Monk, Sonny Rollins, Canonball Aderly, and so on. http://www.directededge.com/?Miles%20Davis http://www.directededge.com/?Miles%20Davis The search end of the spectrum is about finding something you're looking for -- recommendations are about discovering things you didn't know about. The two meet in the middle with "personalized search". If I type "python" into a search engine, do I want snakes or code? You can probably figure that out based on what I've done in the past.
joubert 17y ago
Are there any objective measurement of the quality of recommendations across various recommendation systems?
- ntoshev 17y ago
  There is more than one, depending on the problem and what exactly do you want to improve. For example RMSE is the measurement used for the Netflix Prize.
  - wheels 17y ago
    I really liked Greg's commentary on RMSE -- summed up my thoughts better than I could: http://glinden.blogspot.com/2009/03/what-is-good-recommendation-algorithm.html http://glinden.blogspot.com/2009/03/what-is-good-recommendat...
keefe 17y ago
"....we’ve gone from having a graph-store to having a proper graph database..." A graph database and a "triple store" in semantic technologies are essentially the same thing. This company makes some very aggressive claims that allegrograph, Jena, Oracle (with Spatial), sesame and others (including the korean arm of my current company) have also made. Typically, such claims fail to live up to the marketing. I wonder how this solution compares to these traditional triple stores?
nrao123 17y ago
I have been following the "reccomendation algos as a service" for about 2 years now. This definetely seems interesting but a side opportunity could be to do aggregator/optimizer of recco algos for merchant/publishers similar to what Rubicon Project/Pubmatic does on aggregating/optimizing ad networks. The reccoAlgo aggregatot would take all the various recco algo services such as DirectedEdge, Aggregate Knowledge, Loomia, Minekey, Persai (now dead) and many others and keep running tests (similar to the netflix prize) and whatever is better is given more airtime on suggesting related products/pages for retailers/publishers. The compensation model would work on a percentage of revenue for additional clicks/purchases on the suggestions.
sethorion 17y ago
Question: Greg Linden, who worked on Amazon.com's recommendation engine, has referred to what he calls the "harry potter problem". To quote from his blog: '...this calculation would seem to suffer from what we used to call the "Harry Potter problem", so-called because everyone who buys any book, even books like Applied Cryptography, probably also has bought Harry Potter. Not compensating for that issue almost certainly would reduce the effectiveness of the recommendations, especially since the recommendations from the two clustering methods likely also would have a tendency toward popular items.' How did you compensate for this problem? Do you simply ignore vertices in the graph that have a large degree? Or, are you using non-linear weighting functions, such as a perceptron's sigmoid function? With regard to Wikipedia, almost everyone who has edited an article has also edited the article on Bill Clinton. So, if you are using the edit-history metadata to compute recommendations, you would have to compensate for the "Bill Clinton problem".

108 points by GVRV 17y ago

pclark 17y ago

Hacker News user "Wheels" is a founder - congrats on the write up. Didn't realize you were YC funded

davidw 17y ago

Wow, congrats to wheels & co for the TC hit and YC - I don't think anyone knew about that!

rwolf 17y ago

"We can take data sets with millions and millions of data points and figure out what’s related to a given item in a few milliseconds. Most recommendations engines pre-compute stuff rather than generating the recommendations in real-time like we do" I've been looking into recommendation algorithms recently (started with the excellent book "Programming Collective Intelligence"), and this sounds lightyears ahead of the way we currently do things. I suppose you could take one of the algorithms that requires pre-computing and throw resources at it, but it seems like they are talking about something new. Since I'm just getting started, I'd like to find some academic (or blog-faux-academic) articles on whatever recent advances behind recommendations without the need for precomputing. Anyone know where to look?

10ren 17y ago
They have a couple of articles on their site: http://directededge.com/tech.html http://directededge.com/tech.html
- rwolf 17y ago
  Thanks, you replied right before I was going to edit in the answer from http://developer.directededge.com/article/Introduction_to_Recommendations http://developer.directededge.com/article/Introduction_to_Re...: "Graph-based recommendation systems". Something new to learn! edit: the linked page mentions the book I'm reading! Looks like the answer was a few pages further in :)
ntoshev 17y ago
There is no magic. I don't know what Directed Edge are doing, but simpler Amazon-style recommendations (people who bought X also bought...) doesn't need to precompute anything if you choose your data structures properly: get a list of things people bought along with X, count them (or actually store them counted), optionally normalize for general popularity, sort in decreasing order, show top results. I'd love to hear something from wheels though.
- paraschopra 17y ago
  So, you presume doing all this does not take any time for millions of objects?
  - deleted 17y ago
    [deleted]
  - ntoshev 17y ago
    All I'm saying is that it's possible with the right data structures. It's not more than what a typical search engine does.
- wheels 17y ago
  Amazon's algorithms are simpler than our own (at least as far as I can tell) and most recommendation engines use some sort of embedding to reduce the dimensionality of the problem. Amazon's related products do in fact seem to be, or very near to, a simple counting structure. Our ranking algorithm builds a large subgraph around an item and then does a few passes with a couple different ranking schemes to try to figure out the hot items within that subgraph, prunes "noisy" connections (i.e. links that are "hot", but don't actually pack much semantic meaning) and then tries to scale things so that the results returned aren't simply those with the largest overlap, but those that are most relevant within that subgraph relative to the larger graph. In that sense, it has some similarities to web-search algorithms. In user-visible terms, that means that our results are often less obvious than Amazon's recommendations -- for a long time we called that the "tell me something I don't know" problem. It's no good to do a search for "Miles Davis" and have "Jazz" com back as a related item. If you know about Miles Davis, you already know about Jazz.

bandris 17y ago

Also congrats for DirectedEdge for doing the animation without flash on the front page. Nice!

wheels 17y ago
You can actually search in the little bar there for any wikipedia article to show related stuff, and as kind of a little easter egg, you can specify a starting page via the query string, e.g. http://www.directededge.com/?Y%20Combinator http://www.directededge.com/?Y%20Combinator
- djuedemann 17y ago
  This little wikipedia search bar is hiding nodes from the graph most of the time. It bothers me a little.
  - wheels 17y ago
    We tried a few combinations -- and may try some yet. If it was further right it threw off the balance of the layout. If it was up at the top it drew too much attention to itself. We tried messing with the z-index on hover too, but that looked funky.
    - djuedemann 17y ago
      I thought it would be nice if the user could drag it around.
leif 17y ago
Haha Paul was disconnected from Jessica for a bit. A few more clicks and they were back together. Awww.

utnick 17y ago

cool, have you thought about entering it in any online recommendation contests? ( like netflix or the github one )

mattmaroon 17y ago

"Directed Edge truly believes that we’re about to see a shift on the web away from search and towards recommendations." The difference is somewhat arbitrary when you think about it. When I Google, I'm asking it to recommend me stuff related to what I'm looking for. Google is nothing but the world's best recommendation engine. There are about 1,000 sites that could use good recommendation technology to enhance their profits though, so I like this company's monetization chances. Easy elevator pitch too: recommendations as a service.

ntoshev 17y ago
You are right, there is a continuum of possibilities between pure search and pure recommendation systems. The difference is in the input data that the search/recommendation engine considers: the input in classic search is only the few words in the query, while a recommendation engine considers some history of your interactions with a site or the internet (and nothing that indicates your intent in this particular instance). Google, of course, keeps search history for people logged in. But it doesn't seem to affect their relevancy algorithm much, unlike, say, your location and language. I'd speculate Google Checkout is mostly about gathering data that would allow for meaningful recommendations.
etherealG 17y ago
I was about to post this too. Surely all the possible nodes include all possible search terms, as well as all web pages. Then search and relations are synonymous. Any thoughts on how your system would be different from a search engine in that sense Wheels?
wheels 17y ago
It's a continuum, as hinted below, but the results are pretty different in the polar cases. I'll fall back to my Miles Davis example. Searching for "Miles Davis" returns 10 pages about Miles Davis. Hitting our engine with Wikipedia data for Miles Davis gives you John Coltrane, Herbie Hancock, Wayne Shorter, Thelonious Monk, Sonny Rollins, Canonball Aderly, and so on. http://www.directededge.com/?Miles%20Davis http://www.directededge.com/?Miles%20Davis The search end of the spectrum is about finding something you're looking for -- recommendations are about discovering things you didn't know about. The two meet in the middle with "personalized search". If I type "python" into a search engine, do I want snakes or code? You can probably figure that out based on what I've done in the past.

joubert 17y ago

Are there any objective measurement of the quality of recommendations across various recommendation systems?

ntoshev 17y ago
There is more than one, depending on the problem and what exactly do you want to improve. For example RMSE is the measurement used for the Netflix Prize.
- wheels 17y ago
  I really liked Greg's commentary on RMSE -- summed up my thoughts better than I could: http://glinden.blogspot.com/2009/03/what-is-good-recommendation-algorithm.html http://glinden.blogspot.com/2009/03/what-is-good-recommendat...

keefe 17y ago

"....we’ve gone from having a graph-store to having a proper graph database..." A graph database and a "triple store" in semantic technologies are essentially the same thing. This company makes some very aggressive claims that allegrograph, Jena, Oracle (with Spatial), sesame and others (including the korean arm of my current company) have also made. Typically, such claims fail to live up to the marketing. I wonder how this solution compares to these traditional triple stores?

nrao123 17y ago

I have been following the "reccomendation algos as a service" for about 2 years now. This definetely seems interesting but a side opportunity could be to do aggregator/optimizer of recco algos for merchant/publishers similar to what Rubicon Project/Pubmatic does on aggregating/optimizing ad networks. The reccoAlgo aggregatot would take all the various recco algo services such as DirectedEdge, Aggregate Knowledge, Loomia, Minekey, Persai (now dead) and many others and keep running tests (similar to the netflix prize) and whatever is better is given more airtime on suggesting related products/pages for retailers/publishers. The compensation model would work on a percentage of revenue for additional clicks/purchases on the suggestions.

sethorion 17y ago

Question: Greg Linden, who worked on Amazon.com's recommendation engine, has referred to what he calls the "harry potter problem". To quote from his blog: '...this calculation would seem to suffer from what we used to call the "Harry Potter problem", so-called because everyone who buys any book, even books like Applied Cryptography, probably also has bought Harry Potter. Not compensating for that issue almost certainly would reduce the effectiveness of the recommendations, especially since the recommendations from the two clustering methods likely also would have a tendency toward popular items.' How did you compensate for this problem? Do you simply ignore vertices in the graph that have a large degree? Or, are you using non-linear weighting functions, such as a perceptron's sigmoid function? With regard to Wikipedia, almost everyone who has edited an article has also edited the article on Bill Clinton. So, if you are using the edit-history metadata to compute recommendations, you would have to compensate for the "Bill Clinton problem".