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<[email protected]>:
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<[email protected]> 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
