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https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Peng Cheng updated MAHOUT-1286:
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Fix Version/s: 0.9
Labels: collaborative-filtering datamodel patch recommender (was: )
Status: Patch Available (was: Open)
According to my test, it can load the entire Netflix dataset into memory using
only 3G heap space.
> Memory-efficient DataModel, supporting fast online updates and element-wise
> iteration
> -------------------------------------------------------------------------------------
>
> Key: MAHOUT-1286
> URL: https://issues.apache.org/jira/browse/MAHOUT-1286
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Affects Versions: 0.9
> Reporter: Peng Cheng
> Assignee: Sean Owen
> Labels: patch, collaborative-filtering, datamodel, recommender
> Fix For: 0.9
>
> Attachments: InMemoryDataModel.java, InMemoryDataModelTest.java
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> Most DataModel implementation in current CF component use hash map to enable
> fast 2d indexing and update. This is not memory-efficient for big data set.
> e.g. Netflix prize dataset takes 11G heap space as a FileDataModel.
> Improved implementation of DataModel should use more compact data structure
> (like arrays), this can trade a little of time complexity in 2d indexing for
> vast improvement in memory efficiency. In addition, any online recommender or
> online-to-batch converted recommender will not be affected by this in
> training process.
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