[Sigia-l] Will she be fired?

[Dmitry Nekrasovski]

Apart from questions of whether this tool is effective, and with all
due respect to Susan and MSR, the Michael Jordan usage scenario seems
to me a case of a solution looking for a problem, for a couple of
reasons:

1) Most people who have used a modern search engine for any amount of
time are probably aware that the way to resolve this dichotomy is to
add more terms to their search query, i.e. "michael jordan
basketball", "michael jordan machine learning", etc.

2) If the user is indeed a machine learning expert with no interest in
basketball, the inference the tool makes based on hard drive data is
highly likely to be relevant. However, I can think of equally likely
(or unlikely) usage scenarios (e.g. a Java developer who is also an
amateur astronomer searching for "eclipse"), where this may not be the
case.

Dmitry

On 3/7/07, Ziya Oz <listera at earthlink.net> wrote:
> Susan Dumais, a veteran Microsoft search expert, has built a tool to help
> determine relevance called Personalized Search. It pulls together several
> hundred results and then compares them with the index that Windows users can
> build of the documents on their hard drives, a feature called Desktop
> Search.
>
> She demonstrated the effectiveness of the program by searching for Michael
> Jordan. By culling through local information on her hard drive, the program
> was able to discern that she was interested in finding the Michael Jordan
> who is the machine-learning expert at the University of California,
> Berkeley, not the basketball player.
>
> Search in the future will look nothing like today¹s simple search engine
> interfaces, she said, adding, ³If in 10 years we are still using a
> rectangular box and a list of results, I should be fired.²
>
> <http://www.nytimes.com/2007/03/07/business/07soft.html>
>
> ----
> Ziya
>
> Heterogeneity happens.
>
>
>
>
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