Yes, this is a sketch of basic user-based collaborative filtering, using a cosine-measure correlation as a similarity metric? (I think it needs to divide out by the size of the two vectors?).
The analog in Mahout would be org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender, and org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity I agree that one could parallelize computation of the user-user similarity. Indeed I think any scalable recommender is going to have to do a lot of intense precomputation, via something like Hadoop, and then relatively little at runtime. On Mon, Sep 1, 2008 at 7:38 AM, Edward J. Yoon <[EMAIL PROTECTED]> wrote: > BTW, We also think about CF for example - > http://wiki.apache.org/hama/TraditionalCollaborativeFiltering > > If you have some advanced idea, please let me know.
