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From Sebastian Schelter <...@apache.org>
Subject Re: MySQLJDBCDataModel vs FileDataModel
Date Mon, 04 Jul 2011 17:59:20 GMT
If the item similarities are already precomputed there's no sense in
fetching them from the data model, you can just read use the already
precomputed set of possibly similar items as no other items can be
recommended anyway and it's faster to fetch them from a similarity
implementation that holds them in memory then from any data model
implementation.

--sebastian

2011/7/4 Mark <static.void.dev@gmail.com>:
> May I ask why you choose to go with AllSimilarItemsCandidateItemsStrategy
> over the default PreferredItemsNeighborhoodCandidateItemsStrategy?
>
> On 7/4/11 10:23 AM, Sebastian Schelter wrote:
>>
>> A look into a recent blogpost of mine might maybe be helpful with
>> choosing the appropriate data access strategies for your recommender
>> setup. It covers a very common usecase in great detail:
>>
>>
>> http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/
>>
>> --sebastian
>>
>> 2011/7/4 Mark<static.void.dev@gmail.com>:
>>>
>>> I wouldn't use the in memory JDBC solution.
>>>
>>> I was wondering do most people choose the JDBC backed solutions or the
>>> File
>>> backed?
>>>
>>> On 7/4/11 10:17 AM, Sean Owen wrote:
>>>>
>>>> Yes. Both are just fine to use in production. For speed and avoiding
>>>> abuse
>>>> of the database, I'd load into memory and tell it to periodically
>>>> reload.
>>>> But that too is a bit of a choice between how often you want to consume
>>>> new
>>>> data and how much work you want to do to recompute new values.
>>>>
>>>> On Mon, Jul 4, 2011 at 6:13 PM, Mark<static.void.dev@gmail.com>
>>>>  wrote:
>>>>
>>>>> Ahh ok. So if I want everything in memory like the file backed solution
>>>>> I
>>>>> should use ReloadFromJDBCDataModel? I'm going to give that a try right
>>>>> now.
>>>>>
>>>>> Typically which solution is recommended for production use?
>>>>>
>>>>> Thanks
>>>>>
>>>>>
>

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