[jira] Updated: (MAHOUT-445) Customizable strategies for candidate item fetching

["Sebastian Schelter (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-445?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sebastian Schelter updated MAHOUT-445:
--------------------------------------

    Attachment: MAHOUT-445.patch

> 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.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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