21–30 of 49 posts
So there's some shitty retailer. What does this have to do with algorithmic search?
I don't know if Skrenta's approach is perfect (can spammers make slashtags? I'll bet they can!) but Google's is clearly failing. Giant swathes of Google searches are now overrun with datafog spammers. Ehow, squidoo, hubpages, wikihow, buzzle, how-wiki, ezinearticles, bukisa, wisegeek, articlesnatch, healthblurbs, associatedcontent - all thee and thousands more domains filled with spam semi-automatically generated by…
I invoke SandGorgon’s law of outsourcing analogies As an online discussion about PROGRAMMING grows longer, the probability of a comparison involving outsourcing or Indians approaches 1, if Godwin’s law has not already been satisfied
There is little rigor behind most of the claims of the NYTime story: The targeted site already negates any pagerank benefit of their links (they do implement nofollow), and the definitive example seems to be nothing more than good SEO of the site in question (most of the other front and second page sites are pretty mediocre as well, clearly with little web competition in the keyword space). In any case, go to a shopp…
A lot of his juice comes from every page (seems to be over 10,000 according to Yahoo Site Explorer) on his site linking with good anchor text to every other page. The fact that he ranks so low (on my Google he's number 6 or so) even with this on such an easy term shows something, doesn't it?
Or maybe our algorithms just aren't good enough. Suppose you use bayesian filtering on the text surrounding the links to determine whether the connection is good or bad. With a reasonable amount of data it should be possible. Note: I'm not an algorithms guy, I do business and strategy and a wee bit of programming, so maybe the example isn't good, but I thinkthe point is.
Determining sentiment (the topic of the NYT piece) is considerably harder though, because it would allow for spammers to write negative articles about a site and link to it and negatively affect its rankings. Also, determining the tone/emotions of a piece of text is probably one of the hardest things to do with textual analysis
As a net addict, I regularly find myself frustrated because I can't figure out how to get meaningful information out of Google instead of sites trying to sell me. And if I can't think off the top of my head of a website that will act as a relevant portal for that kind of info, then there isn't really any alternative to Google.
At least, not that I know of yet: can anyone suggest one?
Google has done amazing things for our ability to get what we want and fast, but it also is slowly eroding our independence from it and our ability to educate ourselves by other means.
Here's hoping they prove worthy stewards once they own all the information on the planet.
Or maybe our algorithms just aren't good enough. Suppose you use bayesian filtering on the text surrounding the links to determine whether the connection is good or bad. With a reasonable amount of data it should be possible. Note: I'm not an algorithms guy, I do business and strategy and a wee bit of programming, so maybe the example isn't good, but I thinkthe point is.
Interesting, do people frequently use bayes' theorem in web programming? Ive only seen it it other programming contexts.
http://oreilly.com/catalog/9780596529321
Naive Bayesian classifiers are just one of the more popular types; others include Support Vector Machines (SVMs), decision trees (and their relatives, random forests), and a bunch more. If you'd like to play around with some, Weka is good open source software for this:
it is sinking, but for a different reason. the web is getting away from google. getting locked up in apps, or walled gardens like facebook or itunes
Or maybe our algorithms just aren't good enough. Suppose you use bayesian filtering on the text surrounding the links to determine whether the connection is good or bad. With a reasonable amount of data it should be possible. Note: I'm not an algorithms guy, I do business and strategy and a wee bit of programming, so maybe the example isn't good, but I thinkthe point is.
Google already analyzes backlinks in their context to determine how relevant the anchor text is to the topic of the page. Determining sentiment (the topic of the NYT piece) is considerably harder though, because it would allow for spammers to write negative articles about a site and link to it and negatively affect its rankings. Also, determining the tone/emotions of a piece of text is probably one of the hardest thi…
This could be solved by making sentiments act as a weight (i.e. a multiplier in [0, 1]). Positive sentiments would give a particular reference more weight, negative sentiments would give little to no weight. Then it would be impossible to negatively affect a site's rankings - only positively affect them. Just like now.
Or maybe our algorithms just aren't good enough. Suppose you use bayesian filtering on the text surrounding the links to determine whether the connection is good or bad. With a reasonable amount of data it should be possible. Note: I'm not an algorithms guy, I do business and strategy and a wee bit of programming, so maybe the example isn't good, but I thinkthe point is.
Google already analyzes backlinks in their context to determine how relevant the anchor text is to the topic of the page. Determining sentiment (the topic of the NYT piece) is considerably harder though, because it would allow for spammers to write negative articles about a site and link to it and negatively affect its rankings. Also, determining the tone/emotions of a piece of text is probably one of the hardest thi…
Now if you tell me that there is value in social search we could have a totally different discussion, but it's more about the persuasive power of personal recommendation than algorithms not working any more.