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https://issues.apache.org/jira/browse/MAHOUT-445?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12894181#action_12894181
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Sean Owen commented on MAHOUT-445:
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I like the patch. For the sampling implementation, it's not quite sampling
randomly? just taking the first few? That seems less than ideal. There is a
SamplingIterator and counterpart for long primitives that could be useful here.
I suppose all Recommender implemeentations should have at least one constructor
now that takes the strategy object as a param?
> Customizable strategies for candidate item fetching
> ---------------------------------------------------
>
> Key: MAHOUT-445
> URL: https://issues.apache.org/jira/browse/MAHOUT-445
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Reporter: Sebastian Schelter
> Attachments: MAHOUT-445-2.patch, MAHOUT-445.patch
>
>
> At the beginning of the recommendation process, a recommender has to identify
> a set of "candidate items" which are items that could possibly be recommended
> to the user, the final result of the recommender's computation will be a
> subset of those.
> The current approach in AbstractRecommender.getAllOtherItems(...) turns out
> to be very slow if there is a high number of cooccurrences in the data (like
> in the grouplens 1M dataset for example). The aim of this patch is to make
> the way in which these candidate items are identified customizable.
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