mahout-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Sean Owen (JIRA)" <>
Subject [jira] [Commented] (MAHOUT-1069) Multi-target, side-info aware, SGD-based recommender algorithms, examples, and tools to run
Date Tue, 18 Sep 2012 12:23:07 GMT


Sean Owen commented on MAHOUT-1069:

I imagine this is all great work. As I commented off-list, it is a big enough and even different
enough beast that it feels like it should be a separate project. The Mahout code base is already
uneven and sprawling and I think this would exacerbate that -- and not generate much "synergy"
worth the effort of integration.
> Multi-target, side-info aware, SGD-based recommender algorithms, examples, and tools
to run
> -------------------------------------------------------------------------------------------
>                 Key: MAHOUT-1069
>                 URL:
>             Project: Mahout
>          Issue Type: Improvement
>          Components: CLI, Collaborative Filtering
>    Affects Versions: 0.8
>            Reporter: Gokhan Capan
>            Assignee: Sean Owen
>              Labels: cf, improvement, sgd
>         Attachments: 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 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
> # 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.

This message is automatically generated by JIRA.
If you think it was sent incorrectly, please contact your JIRA administrators
For more information on JIRA, see:

View raw message