[
https://issues.apache.org/jira/browse/MAHOUT-1004?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13814822#comment-13814822
]
Anusha R commented on MAHOUT-1004:
----------------------------------
Okay.. but is it being implemented based on the similar approach as item based
similarity
where item co-occurence matrix is multiplied with item-user vector,
So for user similarity:
Is it user co-occurence matrix multiplied with user-item preference vectot?
Also is development code available in github?
Also if distributed user based similarity development is going on, i would like
to suggest a link, if you find it useful.
http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?arnumber=5432528
> Distributed User-based Collaborative Filtering
> ----------------------------------------------
>
> Key: MAHOUT-1004
> URL: https://issues.apache.org/jira/browse/MAHOUT-1004
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Affects Versions: 0.7
> Reporter: Kris Jack
> Priority: Minor
> Labels: Recommender, User-based
> Fix For: Backlog
>
> Attachments: MAHOUT-1004-trunk-rebased-but-tests-fail.patch,
> MAHOUT-1004.patch
>
> Original Estimate: 336h
> Remaining Estimate: 336h
>
> I'd like to contribute code that implements a distributed user-based
> collaborative filtering algorithm.
> In brief, so far I've taken the code for the existing
> org.apache.mahout.cf.taste.hadoop.item.RecommenderJob and created a new
> org.apache.mahout.cf.taste.hadoop.user.RecommenderJob. With help from Sean
> Owen, I followed a similar approach to the item-based implementation, but
> multiplied a user-user matrix with a user-item vector rather than an
> item-item matrix with an item-user vector. The result of the multiplication
> then needs to be transposed in order to output recommendations by user id.
--
This message was sent by Atlassian JIRA
(v6.1#6144)
