MSN’s ranking algorithm seems to put a lot of emphasis on sheer quantity of incoming links, and very little on link relevance and anchor text.
Microsoft’s search technology puts a huge emphasis on < title > tag, for example, is crucial for getting a good ranking, while the heading tags (< h1 >, < h2 >, etc.) also play a major role.
MSN pay almost no attention to keyword density, meaning that placing a single keyword many times on the same page is probably more likely to help your ranking than hurt it.
Keyword density of over 10%, which would mean almost certain death in Google, will often result in a pleasant boost for your MSN rankings.
Unlike Google’s ranking system, MSN’s algorithm doesn’t seem to a very high keyword density in the < title > tag.
Putting a short and to-the-point primary keyphrase (100% density) as your title is an excellent way to achieve top-three rankings for moderately competitive keywords.
Old and well-established websites have virtually no advantage in Microsoft’s algorithm, meaning that a brand new site can rank well in a matter of days.
Frequently updated sites have a huge advantage. Sites that aren’t changed for a matter of several weeks or months will see their rankings decline noticeably between updates.
Because of this, MSN rankings are much easier to obtain, yet harder to sustain, than rankings in Yahoo or Google.

Optimizing for MSN’s RankNet Technology

The latest major change in the search engine giant, MSN Search, has been the inoculation of “neural networks” into its search engine algorithm, something internal researchers call “RankNet.”
This change took place in late June of this year. This algorithm is fresh, and it is becoming a great consideration for many search optimizers. In this article, Jennifer Sullivan continues her reviews of search engines and their algorithms, this time focussing on MSN's RankNet

MSN RankNet:

RankNet is, in essence, a “learning machine” that takes the patterns of human searches into account, and learns from them, in order to provide more relevant results the next time around.
They start from a baseline of predictions made that are input into its neural net.
Chris Burgess of MSN says,

“We take a bunch of data, ‘propagate’ it through the network (basically, take a bunch of weighted sums of the inputs and munch them together), and get values out of the network.”
They make their predictions with supervised learning, which means,

“…a machine learning technique for creating a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs. The output of the function can be a continuous value (called regression), or can predict a class label of the input object (called classification). The task of the supervised learner is to predict the value of the function for any valid input object after having seen only a small number of training examples (i.e. pairs of input and target output). To achieve this, the learner has to generalize from the presented data to unseen situations in a ‘reasonable’ way.”

Computer-implemented methods are described for, first, characterizing a specific category of information content–pornography, for example–and then accurately identifying instances of that category of content within a real-time media stream, such as a web page, e-mail or other digital dataset.
This content-recognition technology enables a new class of highly scalable applications to manage such content, including filtering, classifying, prioritizing, tracking, etc.
An illustrative application of the invention is a software product for use in conjunction with web-browser client software for screening access to web pages that contain pornography or other potentially harmful or offensive content.
A target attribute set of regular expression, such as natural language words and/or phrases, is formed by statistical analysis of a number of samples of datasets characterized as “containing,” and another set of samples characterized as “not containing,” the selected category of information content.
This list of expressions is refined by applying correlation analysis to the samples or “training data.”
Neural-network feed-forward techniques are then applied, again using a substantial training dataset, for adaptively assigning relative weights to each of the expressions in the target attribute set, thereby forming an awaited list that is highly predictive of the information content category of interest

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