I had a really interesting chat with one of my colleagues last night about keyword detection, keyword relevance algorithms and in general self learning computer systems for the web. These are growing more and more important in the web today.
The problem that is becoming evident at a low level is described in natural search circles as the rich get richer. Since links exist for ever older links may be less relevantly describing a site’s content than newer links and yet these links are still used heavily in determining keyword relevance for a page in Google.
In recommends services if I have browsed both good to great and built to last recently amazon will happily recommend the two together. What if a newer book has a stronger affinity with Good to Great and more people start visiting those two books in the same session… due to the massive weight that old affinity already has the more recent change could take months or years to appear.
A suggestion I have heard is to assign all data sets a relevance score and downweight that relevance logarithmically with time… I love that suggestion. Don’t be surprised if the internet back end needs another tweak within the next 2 yrs as the big sites begin to realize their clever technology isn’t quite as clever as it used to be and is getting a little bit too tied to past results and a little slow to pick up on present trends… interestingly close to a human being as they age really!
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