rocess easier.
>>>
>>> Again, sparksql can help you convert most of the logic directly to spark
>>> jobs but I would suggest exploring text indexing technologies too.
>>>
>>> -- ankur
>>> --
>>> From: Сер
rocess easier.
>>
>> Again, sparksql can help you convert most of the logic directly to spark
>> jobs but I would suggest exploring text indexing technologies too.
>>
>> -- ankur
>> ------
>> From: Сергей Мелехин
>> Sent: 5/24
хин
> Sent: 5/24/2015 10:59 PM
> To: user@spark.apache.org
> Subject: Using Spark like a search engine
>
> HI!
> We are developing scoring system for recruitment. Recruiter enters vacancy
> requirements, and we score tens of thousands of CVs to this requirements,
> and retu
Yes, spark will be useful for following areas of your application:
1. Running same function on every CV in parallel and score
2. Improve scoring function by better access to classification and
clustering algorithms, within and beyond mllib.
These are first benefits you can start with and then thin
suggest exploring text indexing technologies too.
-- ankur
-Original Message-
From: "Сергей Мелехин"
Sent: 5/24/2015 10:59 PM
To: "user@spark.apache.org"
Subject: Using Spark like a search engine
HI!
We are developing scoring system for recruitment. Recr
HI!
We are developing scoring system for recruitment. Recruiter enters vacancy
requirements, and we score tens of thousands of CVs to this requirements,
and return e.g. top 10 matches.
We do not use fulltext search and sometimes even dont filter input CVs
prior to scoring (some vacancies do not hav
