Thursday, January 5, 2017

Relevance, Relevance, Relevance,

Question: What do you think is the NUMBER 1 capability in every great search engine?

For me, the answer never changes.

Finding data with search is easy. Finding too much data is unavoidable. Filtering, sorting, prioritizing and ranking to create the most relevant results has been a primary search goal for decades.

How many times have you found the best answer on the 4th page of Google results? If this was common I am sure you would have switched to something else long ago. Getting relevant results - with the best answers first - is what brings you back for more :).

Irrelevant results rapidly erode user confidence. Search engines that provide poor answers take users from hope to despair in only a few clicks.  Once trust is lost, it can be very hard to get back.

Relevance on the Web - Learning from Google

Google has always been the standard bearer for good sets of ranked results. But frankly, they have had an easier job than most enterprise search products. Web content can be sorted and prioritized using straightforward statistics - some as simple as counting the number of sites that point to a given page. Google also gets a boost from hyperlink text that describes a link target. The text in a link literally offers a curated description of the page it points to. Finally, web content is routinely stored with relatively large passages of unstructured text that makes context and meaning easier to determine.

Enterprise Search Analytics can learn from Google (again)

Modern enterprise search systems need to find alternative ways to filter and rank their results. Page popularity and link text aren't nearly enough.  Once again, Google shows us some of the options because they no longer exclusively rely on web ranking methods.  Let's review some of the recent advancements we have all seen but don't necessarily think of as "relevance builders".

Consider a Google search for the single term "mercury".  You get results like:

Google features now include:
  1. Disambiguation of search terms. Note how this is not simply dictionary autocomplete (which is also good). It is a proactive listing of related concepts that answer the question: "Did you mean X?"
  2. Knowledge graph of attributes related to the default concept.  It helps you understand that you are asking about the right thing before even looking at the results.
  3. List of related topics. In other words, "Did you also know X?"
  4. What other people searched for.  Learning from colleagues is often the quickest route to an answer - especially a high value curated answer.
  5. And finally a "feedback" link to make sure items 1-4 are correct.  Identifying inaccurate outcomes is crucial to user happiness (and the underlying machine learning algorithms too).
Oh yeah: there are the search results too.  But you already expected that :)  

It used to be that enterprise search solutions needed to be different than Google. Nowadays, being like Google is a good place to start.

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