I've been thinking about the same thing. We have a set of queries
that defy straightforward linguistics and ranking, like figuring
out how to match "charlie brown" to "It's the Great Pumpkin,
Charlie Brown" in October and to "A Charlie Brown Christmas"
in December.
I don't have any solutions yet, but I recommend analyzing click logs
and looking at queries where the most-clicked item is not #1.
wunder
On 1/27/09 1:06 PM, "Matthew Runo" <mruno@zappos.com> wrote:
> Hello folks!
>
> We've been thinking about ways to improve organic search results for a
> while (really, who hasn't?) and I'd like to get some ideas on ways to
> implement a feedback system that uses user behavior as input.
> Basically, it'd work on the premise that what the user actually
> clicked on is probably a really good match for their search, and
> should be boosted up in the results for that search.
>
> For example, if I search for "rain boots", and really love the 10th
> result down (and show it by clicking on it), then we'd like to capture
> this and use the data to boost up that result //for that search//.
> We've thought about using index time boosts for the documents, but
> that'd boost it regardless of the search terms, which isn't what we
> want. We've thought about using the Elevator handler, but we don't
> really want to force a product to the top - we'd prefer it slowly
> rises over time as more and more people click it from the same search
> terms. Another way might be to stuff the keyword into the document,
> the more times it's in the document the higher it'd score - but
> there's gotta be a better way than that.
>
> Obviously this can't be done 100% in solr - but if anyone had some
> clever ideas about how this might be possible it'd be interesting to
> hear them.
>
> Thanks for your time!
>
> Matthew Runo
> Software Engineer, Zappos.com
> mruno@zappos.com - 702-943-7833
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