My take on e-commerce search. Similarity matching using a vector space based
model, probabilistic or Boolean ranking has not so much importance as compared
to web search or other domains with full-text search. The reason is the
content. Usually very short texts, highly structured, and often not
I am afraid this is not possible, since getting frequencies for phrases is not
possible, unless the phrases are created as tokens (i.e. using n-grams or
shingles) and indexed. If someone has a solution for this, then I am interested
as well.
/JZ
-Original Message-
From: Dmitry Kan [mai
Having the language already separated makes it a lot easier.
You could add the language suffix (e.g. 3 letter with ISO 639-2B
https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) per field where you have
the different languages. Or else you could have copied an entire field to their
language
If you already have the title of the document, then you could run that title as
a new query against the whole index and exclude the source document from the
results as a filter.
You could use the DisMax query parser:
https://cwiki.apache.org/confluence/display/solr/The+DisMax+Query+Parser
And
3 06
2017-08-21 11:07 GMT+02:00 Junte Zhang :
> You have to specify the field where you specified this field analyzer
> in your request. If you use the catch all field by omitting the field,
> it does not use your filter factory.
>
> /JZ
>
> -Original M
You have to specify the field where you specified this field analyzer in your
request. If you use the catch all field by omitting the field, it does not use
your filter factory.
/JZ
-Original Message-
From: Guilleret Florian [mailto:guilleret.flor...@gmail.com]
Sent: Thursday, August 1
