Customizable strategies for candidate item fetching
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Key: MAHOUT-445
URL: https://issues.apache.org/jira/browse/MAHOUT-445
Project: Mahout
Issue Type: Improvement
Components: Collaborative Filtering
Reporter: Sebastian Schelter
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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