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https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13736992#comment-13736992
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Peng Cheng commented on MAHOUT-1286:
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Here is my final solution after numerous experiments: A combination of double
hashing for storing user/item IDs and 2d hopscotch hashing
(http://mcg.cs.tau.ac.il/papers/disc2008-hopscotch.pdf) for storing preferences
as a map from user/item indices in the double hashing table. Hopscotch hashing
maintains strong locality and high load factor, and each dimension uses an
independent hash function. As a result, it can quickly extract a submatrix or
single row or column.
This is the smallest implementation I can think of, apparently only bloom map
can achieve smaller memory footage. But it has many other problems.
> 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
> 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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