June 15, 2008

How text search has evolved over the past 15 years



  • Incorporating user feedback to refine search results, usually indirectly rather than explicitly, making results better through machine learning. [Amazon.com is the most-often cited example of this with it’s “if you like A, you’ll also like B.”] [CAM] Technically, that’s not a search example, but the general point is correct even so.
  • Various approaches to using taxonomies. The better applications use taxonomies as a navigation guide but don’t force it or require administrators to implement it. Vivisimo.com is an example of interesting, automated clustering approach. [CAM] “Faceted search” seems to be the buzzword here. It’s a big part of what I call “structured search.” But taxonomy use is probably more trivial in search than it is in, say, text mining.
  • Context-sensitive search is now an emerging trend. Systems track what users have previously searched for and infer interest in the same domain to refine search result. So if you look for “line” and a system knows you’ve just looked for “tacklebox,” then it infers you mean “fishing line.” Or if you search for bagels and the system knows you are in 20009, it tells you that you can buy them at Comet Liquors (which happens to sell bagels). [CAM] That happens a lot with ad serving. But I’m not convinced it hit actual search until Google’s personal search kicked off, and that was quite recent.


The original blog post did have one error — Sharon’s PhD isn’t in Computational Linguistics, but rather Slavic Linguistics, as I recently noted in my post about text analytics careers for humanities majors.


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