Semantics Web

The business of algorithms

Recovered from the Wayback Machine.

Algorithms are big business. Recently I’ve seen several jobs where the company wants someone who is “…good with algorithms”. Microsoft is competing with Google is competing with Yahoo to hire the best algorithm wranglers (which evidently, according to the article, does not mean women). IBM is releasing it’s unstructured data architecture (UIMA), including it’s concept-based search algorithms into open source by year end. Even within weblogging the debate, and the race, is on to find the best algorithms to mine us, otherwise known as the higher income people without lives.

Suddenly, the hip and cool kids on the block can “do” algorithms.

With all this interest, though, is a lot of confusion and misunderstandings, starting with but not limited to, the very concept of algorithm– a concept which is now taking on such mystical properties that those who can “do” algorithms are being vested with an almost god-like prescience. It is time, and past time, to put the brakes on the hyperbole surrounding algorithms.

Starting with the basics: what is an algorithm.

What is an algorithm

An algorithm is nothing more than the description of the steps necessary in order to reach a goal. The goal may be something as simple as baking a cake, or as complex as mapping the gene sequence of humans; however, the concept of algorithm doesn’t change with each goal–only the steps.

You have three apples, and someone asks you for one, how do you know how many apples you have left? Seriously, this is not a joke–simple addition, multiplication, division, and subtraction are algorithms, and each is represented by specific equations that introduce specialized operators. To better see this, sometimes you need to remember what it was like to learn math, and then program this knowledge into a computer.

For instance, add two number: 14 and 17. No, not by memorization — by working out the steps. First you line up the rightmost digits, or the ones column, and perform addition on the numbers in this column: 4 and 7. The act of addition is taking one number, breaking it down into units, and then adding these units to another number: 4 + 1 is 5; 5 + 1 is 6; 6 + 1 is 7, and so on. You know this; you remember your first exposure to a calculating device–your fingers. So add 4 and 7, just like you did when you were younger.

Start with 4, turn down your left thumb, that’s 5. Turn down your left index finger, that’s 6. Turn down your left middle finger, that’s 7. Turn down your left ring finger, that’s 8. Turn down your pinky, and that’s 9. Then switch to the other hand, and continue. Turn down your right thumb, that’s 10. Finally, turn down your right index finger and that makes 11. Stop at this point, because you’ve turned down 7 digits. Of cource, we could have started with 7 and added 4, but chances are when you were younger you started with the number on the left and added the number on the right (though this may change based on culture and language).

So now you have 11. That was an amazing accomplishment. Do it enough times, and you remember the result and you don’t have to turn down fingers when you’re asked to change some figures during, say, a board meeting.

But now you have a problem: you have a value in the ones column, but you also have a value in the tens column. So what you do is ‘carry’ that number over to the tens column, and add it to the other numbers that were already there–leading to addition on three numbers: 1 and 1 and 1. Luckily, the numbers are small or we’d probably have to start removing our shoes.

Combine all these steps into sequence of actions, and you have a very complicated, multi-step algorithm. Extend these same basic steps, and you can also do subtraction, multiplication, and division. In fact, once you managed the algorithm for addition, you had the basic skills necessary in order to work with any algorithm. It is only a few short steps from addition to something like Newton’s Method. The only barrier to taking these few short steps is interest and intimidation: interest, because not everyone really wants to learn how to do Newton’s Method; and intimidation because after a while, it’s a lot easier to define new equations and new operators to represent higher-level algorithms, and our first exposure to these sends many of us running for the door.

Aside from the intimidating equations, that’s all an algorithm really is: a formalization of the steps necessary in order to reach a goal. So when I’m asked on an interview if I can “do” algorithms, in my mind I hear: can you add 17 and 14 without having to take off your shoes? I can then reply without hesitation that yes, I can.

I am now ready to work at Google.

Well, not quite.

Pattern Matching, Hypothesis, and Proofs

Any of us can follow an algorithm if we’re interested and patient enough. But it takes something more to be able to derive the algorithm in the first place.

In his book, “Vision”, David Marr writes about the steps he and his fellow researchers took to discovery a computational theory of vision. The first was to create a representational framework, a hierarchical framework of vision, from the simplest edge detection to more complex visual processes. They then searched for existing algorithms that matched the observed and representational behavior:

These ideas suggest that in order to detect intensity changes efficiently, one should search for a filter that has two salient characteristics. First and foremost, it should be a differential operator, taking either a first or second spatial derivitive of the image. Second, it should be capable of being tuned to act at any desired scale, so that large filters can be used to detect blurry edges, and small ones to detect sharply focused file detail in the image.

Marr and his co-researcher Hildreth, eventually came up with the Laplacian of Gaussian, also known as the Marr Filter, or Marr-Hidreth filter.

Marr and Hildreth were able to derive their filter, their algorithm, because of training in math and neuroscience, as well as new research in the fields of artificial intelligence and vision. The training provided the tools they needed: a catalog of existing algorithms, as well as the necessary protocols; the research then provided the necessary new data.

If you use Photoshop’s Unsharp Mask, you can judge for yourself the success of Marr’s efforts. The point is that Marr and Hildreth established a goal, observed behavior, and then went shopping to find which algorithms came closest to matching the observed behavior — in this case a combination of algorithms: applying a Guassian filter to blur the image and remove structure, and the Laplacian to detect the ‘edges’ or differences of intensity that remains.

So now if I had the opportunity to work with the late Dr. Marr, and he asked me if I can ‘do’ algorithms, in my mind I hear: “Ohmigod, what am I doing here. Do you think anyone will notice if I slip out?”

Okay, but we’re not trying to invent artificial intelligence here

Now that we’ve gone from adding two apples to programming human sight, we’ll focus on algorithms located somewhere inbetween.

Though I wouldn’t have the background to work directly with Dr. Marr, this isn’t to say I can’t work with algorithms. Anytime I write code I make use of, or even create algorithms. When I work with data, either in RDF format or as relational data, I am using algorithms. As Dr. Marr was an ‘expert’ in computer vision and neuroscience, I’m, equally, an expert in my field of interest.

Most of us work with algorithms, though we may not be aware of the fact. When we follow a recipe for Beef Wellington, the instructions for putting together a model airplane, or to knit a complex baby blanket pattern, we’re following algorithms. And if we create a new recipe, knitting pattern, or computational theory of human behavior, we’re creating new algorithms–usually derived from existing ones, if possible. (It’s easier to work with existing, proven, algorithms then have to go through formal proofs with new ones.)

In other words: there is no ‘algorithm’ gene that some people have, and others don’t. One doesn’t need a PhD to work with algorithms; the ability to work with algorithms is pretty much universal.

Cool. So where was I? Oh yes, the business of algorithms.


I had to do it before someone else did it. It was only a matter of time.

Now that weblogging has established its credibility (i.e. can be used to make money) and there are millions of us (”over 14 million served daily”), the interest in creating algorithms to make use of all the rich, seductive unstructured data we generate is very strong. Understandably so.

However, unlike previous research projects such as Dr. Marr’s, current weblogging effort seems to focus on the algorithms rather than the goal. Because of this, we’re measuring every last bit about ourselves, but not coming up with anything useful. By focusing on the tools rather than the end point we’re mixing search with popularity, marketing with discovery, and then we’re throwing in a little structured data–just to make things interesting.

For instance, looking at mixing search with popularity:

The Technorati 100, Blogdex, and Daypop Top 40 are all representatives of the same general type of algorithm: notification of update, extract out the links, increment the count for any matched link, with the top n number of link holders placed on the list, ordered by number of links. Though the data is treated differently–Technorati persists the number of links per page, while Daypop and Blogdex are only interested in reflecting ‘fresh’ links–the concept, and hence, the algorithm is the same: when a link to a specific page is encountered, add a link to the source to a list, and increment a counter. Then adjust the list accordingly.

None of this activity has anything to do with search. Each tool may also grab data for a search component, but the algorithms for popularity are not specific to search. Where the two get confused is when popularity is used as a factor of search.

Adjusting search results based on popularity is to combine two different algorithms, but there is no rhyme or reason for doing so. The fact that one page is more ‘popular’ than another does not make it a better authority. It’s not the same as something like Google’s PageRank, because PageRank is not a measure of popularity.

Ian Rogers wrote a very nice writeup on the original PageRank algorithm, breaking down the formula into the various steps. Summarizing, PageRank is based on incoming links, but it is the value of the links that helps push up the PageRank, and this value is dependent on how many outgoing links a page has.

This sounds like popularity but it isn’t. The whole purpose of the PageRank algorithm is to approximate a random surfer, and the probability that they would end up at the page after randomly clicking through so many pages. According to the Sergey Brin and Lawrence Page’s original page:

PageRank can be thought of as a model of user behavior. We assume there is a “random surfer” who is given a web page at random and keeps clicking on links, never hitting “back” but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its PageRank. And, the d damping factor is the probability at each page the “random surfer” will get bored and request another random page. One important variation is to only add the damping factor d to a single page, or a group of pages. This allows for personalization and can make it nearly impossible to deliberately mislead the system in order to get a higher ranking.

The PageRank in use today is not the same one just described; the one in use today is said to feature over 100 different variables. It’s not surprising that the computation has changed, but we have to suppose that the reasoning remains: it’s purpose is not to calculate popularity, but to capture the behavior of the random surfer. The only problem is, webloggers are not random surfers.

Weblogs combine the threaded chat behavior of a bulletin board system, with the separate domains of more traditional web pages. As such, the genre creates a threaded web linking behavior that must play havoc with the traditional search engine paradigms. We don’t link just because of interest or as reference; we also link based on a personal attachments, likes/dislikes, as part of self or other promotion, and a host of other reasons, none of which had anything to do with the traditional web linking of long ago.

danah boyd discussed this in a recent post, after reviewing 400 weblogs for linking patterns. She wrote:

Linking Patterns:

The Top 100 tend to link to mainstream media, companies or websites (like Wikipedia, IMDB) more than to other blogs (Boing Boing is an exception).

Blogs on blogging services rarely link to blogs in the posts (even when they are talking about other friends who are in their blogroll or friends’ list). It looks like there’s a gender split in tool use; Mena said that LJ is like 75% female, while Typepad and Moveable Type have far fewer women.

Bloggers often talk about other people without linking to their blog (as though the audience would know the blog based on the person). For example, a blogger might talk about Halley Suitt’s presence or comments at Blogher but never link to her. This is much rarer in the Top 100 who tend to link to people when they reference them.

Content type is correlated with link structure (personal blogs contain few links, politics blogs contain lots of links). There’s a gender split in content type.

When bloggers link to another blog, it is more likely to be same gender.

As danah mentioned, 400 weblogs is too few to extrapolate any global behavior, but we’ve seen one or more of these ourselves–in particular not linking to someone but giving the person’s name; or not even directly mentioning a name (a behavior that is becoming more common).

Whether there are gender differences in linking has been the subject of much debate. danah found in her examination of 400 weblogs that gender linked to like gender more often than not. If this is true, and women account for about 50% of the weblogs, then we should expect more weblogs by women in the popularity lists. That we don’t shows that we need to continue our observation before we can begin to derive algorithms related to weblogging and popularity, and weblogging and search.

As for marketing and discovery, and the thin vein of structured data (microformats, syndication feeds, FOAF, et al) that runs through all of this unstructured mess–this will be good for a follow on topic someday.


Mary Hodder had originally listed out several different metrics for consideration when it comes to developing an algorithm and asked if the approach she proposed was the right one:

So this is my first post think about making an open source algorithm. And I’m wondering, is this a useful approach? I think it could be worthwhile, done right, and I put it out there to the blogging community to determine what is best here. As I said, after seeing what people who want to work with smaller topic communities are doing, it may be in blogger’s interest to think about how this might be done so that is it more in keeping with the desires and views of the blogosphere.

The approach–reviewing all the different metrics, looking at representative data, searching for repetitive behaviors–are all good, and, equally, all for nought unless the purpose for the effort is clearly understood. This is is worth repeating: an algorithm is nothing more than a formalization of the steps necessary to meet a goal. And replacing Technorati 100 because it ’sucks’ is not a particuarly good goal. So my suggestion to Mary would be to establish the end points for this effort, first.

And now rumors abound that Technorati is being sold, possibly to a major search company. Well, that’s one way to get rid of the Technorati 100–convert it to the Yahoo 100. Semi-serious joking aside, if we know one thing by now about all of this, it’s that the unstructured data that weblogging sits on is this year’s hottest commodity–second only in value to the algorithms used to mine it.

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