BA Insight has recently received a patent for a novel approach to improving relevance which I thought, when explained, would help some of you to think about relevance in a different light.

We call our approach AptivRank. The word apt means “quick to learn” and we named it such to indicate it is a self learning-algorithm; not a manual approach to relevance tuning.

In the Enterprise, users don’t link or subscribe to content in the same way so Google’s PageRank simply doesn’t work. So you do you get users to identify relevant content without them feeling burdened?

IT pros are often shocked when a search proof of concept appears highly successful, but when deployed into production users are anything but pleased, and so the inevitable question keeps coming up…”Why can’t it just be like Google?” Users will struggle to explain what they are actually asking for but clearly relevance is a big part of their disappointment. So what went wrong with the POC?

IT will normally conduct tests against a small set of documents in the POC relative to the size of the corpus in production. The problem here is that this can set false expectations around the type of relevance the search engine can serve up…and here is why.

SharePoint 2010 has many new search features. Let me list the few that I think are the most compelling:

Enhanced People Search

One of the most compelling features found in SharePoint Search 2010 is around People Search. In MOSS 2007, users would often have difficulty finding people because they weren’t sure how to spell the person’s name and MOSS lacked wildcard capability. In Addition to that, Faceted Search was not available. Faceted Search is quite useful in that in gives end users the ability to refine a People Search result by department, skill set, job title, etc. These limitations are covered in detail in Martin’s Post. SharePoint 2010 Search now supports Wildcard and Faceted Search out of the box. SharePoint 2010 People Search also supports Phonetic Search which is a really impressive. This feature will search based on how a name sounds. So if I search on Brian, when in fact the users name is Bryan, it will find a match.