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https://issues.apache.org/jira/browse/MAHOUT-1069?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved MAHOUT-1069.
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Resolution: Won't Fix
Good stuff, just probably its own project!
> Multi-target, side-info aware, SGD-based recommender algorithms, examples,
> and tools to run
> -------------------------------------------------------------------------------------------
>
> Key: MAHOUT-1069
> URL: https://issues.apache.org/jira/browse/MAHOUT-1069
> Project: Mahout
> Issue Type: Improvement
> Components: CLI, Collaborative Filtering
> Affects Versions: Backlog
> Reporter: Gokhan Capan
> Labels: cf, improvement, sgd
> Attachments: MAHOUT-1069.patch, MAHOUT-1069.patch
>
> Original Estimate: 168h
> Remaining Estimate: 168h
>
> Upon our conversations on dev-list, I would like to state that I have
> completed the merge of the recommender algorithms that is mentioned in
> http://goo.gl/fh4d9 to mahout.
> These are a set of learning algorithms for matrix factorization based
> recommendation, which are capable of:
> * Recommending multiple targets:
> *# Numerical Recommendation with OLS Regression
> *# Binary Recommendation with Logistic Regression
> *# Multinomial Recommendation with Softmax Regression
> *# Ordinal Recommendation with Proportional Odds Model
> * Leveraging side info in mahout vector format where available
> *# User side information
> *# Item side information
> *# Dynamic side information (side info at feedback moment, such as proximity,
> day of week etc.)
> * Online learning
> Some command-line tools are provided as mahout jobs, for pre-experiment
> utilities and running experiments.
> Evaluation tools for numerical and categorical recommenders are added.
> A simple example for Movielens-1M data is provided, and it achieved pretty
> good results (0.851 RMSE in a randomly generated test data after some
> validation to determine learning and regularization rates on a separate
> validation data)
> There is no modification in the existing Mahout code, except the added lines
> in driver.class.props for command-line tools. However, that became a huge
> patch with dozens of new source files.
> These algorithms are highly inspired from various influential Recommender
> System papers, especially Yehuda Koren's. For example, the Ordinal model is
> from Koren's OrdRec paper, except the cuts are not user-specific but global.
> Left for future:
> # The core algorithms are tested, but there probably exists some parts those
> tests do not cover. I saw many of those in action without problem, but I am
> going to add new tests regularly.
> # Not all algorithms have been tried on appropriate datasets, and they may
> need some improvement. However, I use the algorithms also for my M.Sc.
> thesis, which means I will eventually submit more experiments. As the
> experimenting infrastructure exists, I believe community may provide more
> experiments, too.
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