At the recent PHP UK Conference 2012 I had the opportunity to chat about machine learning and IR with a bunch of very smart people. One of the conversations included the always enlightening Rowan Merewood, and was around ranking Twitter friends. It’s reasonably well known that Google used to use a variant of PageRank based on who-follows-who to rank it’s Twitter search results (back when it had them). The question is, could the same kind of thing work over a much smaller set - say using it to rank the influence users I follow, in order, perhaps, to prioritise tweets?

I had a great time at the recent PHP Benelux Conference in Belgium. There was a real mix of very interesting people to talk to, and I came away from it buzzing with new ideas (and a ridiculously long todo list). Some of the conversations I had during the weekend were around technical presenting at conferences and usergroups, so I thought I’d collect a handful of the tips that were discussed into a post, and use a few of my favourite speakers at the event to illustrate them.

A lot of interesting techniques involve taking statistical samples, and using those to predict what we’ll see in the future. Usually this works pretty well, but when we’re dealing with a lot of options or if we have some options that are very rare that approach can go pretty wrong. If we go down the street and note down how many men and women we see, we’ll probably be able to use that to predict the chance of the next person we see being male or female pretty well. However, if we were counting all the species of animals we encounter, and trying to use that to predict what we’ll see in the future, we’d likely run in to a couple of problems.

In the last post we had a simple stepping algorithm, and a gradient descent implementation, for fitting a line to a set of points with one variable and one ‘outcome’. As I mentioned though, it’s fairly straightforward to extend that to multiple variables, and even to curves, rather than just straight lines.

I’ve had a couple of emails recently about the excellent Stanford Machine Learning and AI online classes, so I thought I’d put up the odd post or two on some of the techniques they cover, and what they might look like in PHP.

For my own benefit, if nothing else, since I keep seeming to need this snippet of code, I thought I’d encapsulate a Xapian More Like This/Find Similar example in a very brief blog post.

Benfords Law is not an exciting new John Nettles based detective show, but an interesting observation about the distribution of the first digit in sets of numbers originating from various processes. It says, roughly, that in a big collection of data you should expect to see a number starting with 1 about 30% of the time, but starting with 9 only about 5% of the time. Precisely, the proportion for a given digit can be worked out as:

Monte Carlo simulations are a handy tool for looking at situations that have some aspect of uncertainty, by modelling them with a pseudo-random element and conducting a large number of trials. There isn’t a hard and fast Monte Carlo algorithm, but the process generally goes: start with a situation you wish to model, write a program to describe it that includes a random input, run that program many times, and look at the results.

The web is a great place for people to express their opinions, on just about any subject. Even the professionally opinionated, like movie reviewers, have blogs where the public can comment and respond with what they think, and there are a number of sites that deal in nothing more than this. The ability to automatically extract people’s opinions from all this raw text can be a very powerful one, and it’s a well studied area - no doubt because of the commercial possibilities.

Google was a better search engine than it’s predecessors for a number of reasons, but probably the most well known one is PageRank, the algorithm for measuring the importance of a page based on what links to it. Though not necessarily that useful on its own, this kind of link analysis can be very helpful as part of a general information retrieval system, or when looking at any kind of network, such as a friend graph from a social network.