Setting mm to 100% means that any misspelled word in a query means zero
results. That is not a good experience. Usually, 10% of queries contain a
misspelling.
Set mm to 1.
The F-measure is not a good choice for this because recall is not very
important in e-commerce. Use precision-oriented mea
Thanks for all the info, I really appreciate your help. I'm working on the
configuration and following your suggestions.
We already had a golden set of query-results pairs (~1000) used to tune and
check how my application (and Solr configuration) performs.
But I've to entirely double check if this
That page from Stanford is not about e-commerce search. Westlaw is professional
librarian search.
I agree with Emir’s advice. Start with edismax. Use a small value for the
tie-breaker. It is one of the least important configuration values. I use the
default from the sample configs:
0.1
synonyms and relations between
> search terms.
>
> /JZ
>
> -Original Message-
> From: Charlie Hull [mailto:char...@flax.co.uk]
> Sent: Tuesday, October 17, 2017 10:10 AM
> To: solr-user@lucene.apache.org
> Subject: Re: E-Commerce Search: tf-idf, tie-break and
between search terms.
/JZ
-Original Message-
From: Charlie Hull [mailto:char...@flax.co.uk]
Sent: Tuesday, October 17, 2017 10:10 AM
To: solr-user@lucene.apache.org
Subject: Re: E-Commerce Search: tf-idf, tie-break and boolean model
For our e-commerce customers we've been recomme
For our e-commerce customers we've been recommending a test-based
relevance tuning strategy: here's a series of blogs written for us by
someone who ran search for the world's largest electronic component
distributor:
http://www.flax.co.uk/blog/2016/03/18/get-started-improving-site-search-releva
I was having the discussion with a colleague of mine recently, about
E-commerce search.
Of course there are tons of things you can do to improve relevancy:
Custom similarity - edismax tuning - basic user events processing - machine
learning integrations - semantic search ect ect
more you do, bette
Hi Vincenzo,
Unless you have really specific ranking requirements, I would not suggest you
to start with you proprietary similarity implementation. In most cases edismax
will be good enough to cover your requirements. It is not easy task to tune
edismax since it has a log knobs that you can use.
