The Science of Better Search

Researchers at the Arizona State University have prototyped an interactive linear programming-based meta-search tool called LaserSearch. The online documentation does not explain how the underlying search is implemented. (Basic search results are similar to, but don’t map exactly to Google or Yahoo output.) LaserSearch implements user-driven iterative refinement: on the basic results page you indicate relevance by clicking green (highly relevant), yellow (somewhat relevant) and red (not relevant) buttons next to each URL. Clicking Improve refines the results. According to one of the authors, Asim Roy, each click on Improve triggers the solution of a small linear programming problems that sifts through the flood of results for high-value URLs.

I have tried out the prototype, but despite the “OR Inside”, I have yet to understand its filtering logic. For instance, here are the top three results of a search for “norwegian wood”.

The first URL points to Murakami’s novel with that title, the second to the Wikipedia entry for the Beatles song, and the third to a video on Youtube. So far so good. Since I was interested in the Beatles song, I clicked the red button (i.e., irrelevant) next to the first entry, the star (indicating “more relevant”, which also activates the green button) for the second, and the green button for the third. Then I clicked on Improve, which brought up the following screen. The top seven results do, in fact, concern the Beatles song. So LaserSearch clearly promoted results related to the Beatles song. However, Murakami’s novel reappears in slot 8, as do two other non-Beatles references in slots 9 and 11.

Shouldn’t the lowest-possible ranking of the novel on the first results screen essentially remove it from succeeding searches? I don’t know. Perhaps further experimentation will yield more comprehensible results.

If you choose to try out LaserSearch – and I encourage you to do so – be warned, the prototype is somewhat fragile. As I was testing it over a period of 30 minutes, it crashed once and generated empty results screens a couple of times. But for an alpha version, it works well enough.

As for understanding its logic, perhaps it is necessary to read the associated paper. Titled An Interactive Search Method Based on User Preferences, it was published in the December 2008 issue of the journal Decision Analysis.

This entry was posted on 18 February 2009 at 23:01 and is filed under Operations Research. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response or trackback from your own site.