Well, actually, I wanted to represent each movie with a vector [1, 0, 0, 1, 0]
Where each column represents an explicit genre, a 1 indicating that the movie has that genre while a 0 indicates it is not (a crude representation, I'm sure) I wanted to implement an item based recommender that uses these vectors to compute similarity between items. I think I figured it out, I could represent vector data as preferences where instead of user ID's, it would be column indices. Then load that into a DataModel for use with the ItemSimilarity object. The ItemBasedRecommender could load the DataModel with userID's while using this ItemSimilarity object for calculating similarities. This could possibly be a poor choice from an efficiency, accuracy, and machine learning standpoint, I am not an expert on the subject at all. On May 8, 2012, at 12:58 AM, Sean Owen wrote: > 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.
