Using MM efficiently to get right number of results

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Using MM efficiently to get right number of results

Nitin Arora-2
I am looking for an efficient way for setting the MM(minimum should match)
parameter for my solr search queries. As we go from MM=100% to MM=0%, we
move from lots of zero result queries on one hand to too many irrelevant
results (which may then get boosted by other factors) on the other. I can
think of multiple ways to approach this:
1) Try decreasing mm from 100% to 90% to 80% in a staggered manner till you
have just the right number of results. Does not sound very efficient though.
2) Use a low value of MM, say 0%, and then pick only the top 200 results to
apply other boost factors to. Would not allow to use bf, bq, boost within
SOLR.

My question is, What is the standard industry practice in this matter. How
do you go about ensuring that your search returns *just the right* number
of results so that you can use other boost functions on the relevant set of
results.

Thanks in advance
Nitin
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Re: Using MM efficiently to get right number of results

Erick Erickson
There isn’t really  an “industry standard”, since the reasons someone
wants this kind of behavior vary from situation to situation. That said,
Solr has RerankQParserPlugin that’s designed for this.

Best,
Erick

> On Feb 10, 2020, at 4:23 AM, Nitin Arora <[hidden email]> wrote:
>
> I am looking for an efficient way for setting the MM(minimum should match)
> parameter for my solr search queries. As we go from MM=100% to MM=0%, we
> move from lots of zero result queries on one hand to too many irrelevant
> results (which may then get boosted by other factors) on the other. I can
> think of multiple ways to approach this:
> 1) Try decreasing mm from 100% to 90% to 80% in a staggered manner till you
> have just the right number of results. Does not sound very efficient though.
> 2) Use a low value of MM, say 0%, and then pick only the top 200 results to
> apply other boost factors to. Would not allow to use bf, bq, boost within
> SOLR.
>
> My question is, What is the standard industry practice in this matter. How
> do you go about ensuring that your search returns *just the right* number
> of results so that you can use other boost functions on the relevant set of
> results.
>
> Thanks in advance
> Nitin

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Re: Using MM efficiently to get right number of results

Nitin Arora-2
Thanks Erick, a follow-up question for RerankQParser:
How complex can the rerank query itself be? Can we add multiple boost
factors based on different conditions - say, if category is X boost by 2,
if brand is Y boost by 3, etc.?

On Mon, 10 Feb 2020 at 18:12, Erick Erickson <[hidden email]>
wrote:

> There isn’t really  an “industry standard”, since the reasons someone
> wants this kind of behavior vary from situation to situation. That said,
> Solr has RerankQParserPlugin that’s designed for this.
>
> Best,
> Erick
>
> > On Feb 10, 2020, at 4:23 AM, Nitin Arora <[hidden email]> wrote:
> >
> > I am looking for an efficient way for setting the MM(minimum should
> match)
> > parameter for my solr search queries. As we go from MM=100% to MM=0%, we
> > move from lots of zero result queries on one hand to too many irrelevant
> > results (which may then get boosted by other factors) on the other. I can
> > think of multiple ways to approach this:
> > 1) Try decreasing mm from 100% to 90% to 80% in a staggered manner till
> you
> > have just the right number of results. Does not sound very efficient
> though.
> > 2) Use a low value of MM, say 0%, and then pick only the top 200 results
> to
> > apply other boost factors to. Would not allow to use bf, bq, boost within
> > SOLR.
> >
> > My question is, What is the standard industry practice in this matter.
> How
> > do you go about ensuring that your search returns *just the right* number
> > of results so that you can use other boost functions on the relevant set
> of
> > results.
> >
> > Thanks in advance
> > Nitin
>
>
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Re: Using MM efficiently to get right number of results

Erick Erickson
It can be basically any thing you can do with a standard Solr query.

> On Feb 13, 2020, at 9:09 AM, Nitin Arora <[hidden email]> wrote:
>
> Thanks Erick, a follow-up question for RerankQParser:
> How complex can the rerank query itself be? Can we add multiple boost
> factors based on different conditions - say, if category is X boost by 2,
> if brand is Y boost by 3, etc.?
>
> On Mon, 10 Feb 2020 at 18:12, Erick Erickson <[hidden email]>
> wrote:
>
>> There isn’t really  an “industry standard”, since the reasons someone
>> wants this kind of behavior vary from situation to situation. That said,
>> Solr has RerankQParserPlugin that’s designed for this.
>>
>> Best,
>> Erick
>>
>>> On Feb 10, 2020, at 4:23 AM, Nitin Arora <[hidden email]> wrote:
>>>
>>> I am looking for an efficient way for setting the MM(minimum should
>> match)
>>> parameter for my solr search queries. As we go from MM=100% to MM=0%, we
>>> move from lots of zero result queries on one hand to too many irrelevant
>>> results (which may then get boosted by other factors) on the other. I can
>>> think of multiple ways to approach this:
>>> 1) Try decreasing mm from 100% to 90% to 80% in a staggered manner till
>> you
>>> have just the right number of results. Does not sound very efficient
>> though.
>>> 2) Use a low value of MM, say 0%, and then pick only the top 200 results
>> to
>>> apply other boost factors to. Would not allow to use bf, bq, boost within
>>> SOLR.
>>>
>>> My question is, What is the standard industry practice in this matter.
>> How
>>> do you go about ensuring that your search returns *just the right* number
>>> of results so that you can use other boost functions on the relevant set
>> of
>>> results.
>>>
>>> Thanks in advance
>>> Nitin
>>
>>