2012/5/1 Kris Jack <[EMAIL PROTECTED]>
> I recently implemented a distributed user-based collaborative filtering
> algorithm. I've tested it experimentally and found that it is better
> to Mendeley's data set for generating recommendations than the item-based
> This is mostly because Mendeley's data set has far more items than users.
> I'd like to contribute this code to the Mahout project. This will be the
> first patch that I write for Mahout so I'm following the instructions at
> 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
> then needs to be transposed in order to output recommendations by user id.
> Rather than changing the item-based code, I've created new classes for the
> user-based version, which tend to be modified versions of the originals.
> would be much tidier to merge these together, where possible, and to
> parametrise them. I didn't want to change the item-based code straight
> however, without consulting you all.
> Would be great to get some feedback.
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