So you have already decided, for each movie, whether it's in or not in each genre? And then you want to create a "profile" -- assuming you mean some kind of meta-genre?
This isn't a recommender problem; it's just a clustering problem. I'd use the Tanimoto similarity. You could run the clustering-based recommender just to build the clusters. You wouldn't use it for recommendations. On Tue, May 8, 2012 at 8:53 AM, Daniel Quach <[email protected]> wrote: > Suppose that I want to give each movie a profile based on the genres each > contains. > > For naive and simplistic purposes, let's pretend that each movie has a > vector where each column is a genre, a 1 in that column indicates that the > movie contains that genre, 0 otherwise. > > How would I feed such data into an Item-based Recommender? I want this > recommender to use these vectors for calculating similarity for > recommendations, which in turn is used for preference estimation (just as > described in section 4.4.1 of the Mahout in Action book) > > The example in the book is not immediately clear to me. The sample code > does not mention the format of the data being used in creating the > ItemSimilarity object.
