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https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13757801#comment-13757801
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Gokhan Capan commented on MAHOUT-1286:
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Here is what I think:
1- We should implement a matrix that uses your 2d Hopscotch hash table as the
underlying data structure (or the current open addressing hash table
implementation that already exists in Mahout, depending on benchmarks)
2- We should handle concurrency issues that might be introduced by that matrix
implementation
3- We then can replace the FastByIDMap(s) with that matrix, trust at the
underlying matrix for concurrent updates, and never create a PreferenceArray
unless there is an iteration over users (or items)
What do you think?
> 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
> Labels: collaborative-filtering, datamodel, patch, recommender
> Fix For: 0.9
>
> Attachments: InMemoryDataModel.java, InMemoryDataModelTest.java,
> Semifinal-implementation-added.patch
>
> 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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