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https://issues.apache.org/jira/browse/MAHOUT-1286?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13737019#comment-13737019
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Peng Cheng commented on MAHOUT-1286:
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Hi Dr Dunning,
Indeed both Gokhan and me have experimented on that, but I've run into some
difficulties, namely 1) a columnar form doesn't support fast extraction of
rows, yet dataModel should allow quick getPreferencesFromUser() and
getPreferencesForItem(). 2) a columnar form doesn't support fast online update
(time complexity is O(n), maximally O(n) if using block copy and columns are
sorted). 3) To create such dataModel we need to initialize a HashMap first,
this uses twice as much as heap space for initialization, could defeat the
purpose though.
I'm not sure if Gokhan has encountered the same problem. Didn't hear from him
for some time.
The search based recommender is indeed a very tempting solution. I'm very sure
it is an all-improving solution to similarity-based recommenders. But low rank
matrix-factorization based ones should merge preferences from the new users
immediately into the prediction model, of course you can just project it into
the low rank subspace, but this reduces the performance a little bit.
I'm not sure how much Lucene supports online update of indices, but according
to guys I'm working with the online recommender seems to be in demand these
days.
> 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: collaborative-filtering, datamodel, patch, 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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