Chris,
Sounds good! Thanks for the tips.. I'll be glad to submit my talk to this
as I have a writeup pretty much ready to go.
Cheers
Amit
On Tue, Jan 28, 2014 at 11:24 AM, Chris Hostetter
wrote:
>
> : The initial results seem to be kinda promising... of course there are
> many
> : more optimiz
: The initial results seem to be kinda promising... of course there are many
: more optimizations I could do like decay user ratings over time to indicate
: that preferences decay over time so a 5 rating a year ago doesn't count as
: much as a 5 rating today.
:
: Hope this helps others. I'll open
Hi Chris (and others interested in this),
Sorry for dropping off.. I got sidetracked with other work and came back to
this and finally got a V1 of this implemented.
The final process is as follows:
1) Pre-compute the global categorical num_ratings/average/std-dev (so for
Action the average rating
: I thought about that but my concern/question was how. If I used the pow
: function then I'm still boosting the bad categories by a small
: amount..alternatively I could multiply by a negative number but does that
: work as expected?
I'm not sure i understand your concern: negative powers would
I thought about that but my concern/question was how. If I used the pow
function then I'm still boosting the bad categories by a small
amount..alternatively I could multiply by a negative number but does that
work as expected?
I haven't done much with negative boosting except for the sledgehammer
: My approach was something like:
: 1) Look at the categories that the user has preferred and compute the
: z-score
: 2) Pick the top 3 among those
: 3) Use those to boost search results.
I think that totaly makes sense ... the additional bit i was suggesting
that you consider is that instead of
Hey Chris,
Sorry for the delay and thanks for your response. This was inspired by your
talk on boosting and biasing that you presented way back when at a meetup.
I'm glad that my general approach seems to make sense.
My approach was something like:
1) Look at the categories that the user has pref
: I have a question around boosting. I wanted to use the &boost= to write a
: nested query that will boost a document based on categorical preferences.
You have no idea how stoked I am to see you working on this in a real
world application.
: Currently I have the weights set to the z-score equi
Hi all,
I have a question around boosting. I wanted to use the &boost= to write a
nested query that will boost a document based on categorical preferences.
For a movie search for example, say that a user likes drama, comedy, and
action. I could use things like
qq=&q={!boost%20b=$b%20defType=edis
