Enterprise search was once easy. It was often bad - but understanding the results was pretty easy. If the query term(s) were in the document, it was there in the results. Period. The more times the terms appeared, the higher the result appeared in the result list. And when the user typed a multi-term query, documents with all of the terms displayed higher in the result list than those with only some of the terms.
And some search platforms could 'explain' why a particular document was ranked where it was. Those of us who have been in the business a while may remember the Verity Topic and Verity K2 product lines. One of the most advanced capabilities in these was the 'explain' function. It would reverse engineer the score for an individual document and report the critical 'why' the selected document was ranked as it was.
"People Like You"
Now, search results are generally better, but every now and then, you’ll see a result that surprises you, especially on sites that are enhanced with machine learning technologies. A friend of mine tells of a query she did on Google while she was looking for summer clothes, but the top result was a pair of shoes. She related her surprise: "I asked for summer clothes, and Google shows me SHOES?". But, she admitted, "Those shoes ARE pretty nice!"
How did that happen? Somewhere, deep in the data Google maintains on her search history, it concluded that "people like her" purchased that pair of shoes.
In the enterprise, we don't have the volume of content and query activity of the large Internet players, but we do tend to have more focused content and a narrower query vocabulary. ML/AI tools like MLLib, part of both Mahout and Spark, can help our search platforms generate such odd yet often relevant results; but these technologies are still limited when it comes to explaining the 'why' for a given result. And those of us who still exhibit skepticism when it comes to computers, that capability would be nice.
Are you using or planning to implement) ML-in-search? A skeptic? Which camp you're in? Let me hear from you! email@example.com.