On Thu, Feb 11, 2010 at 11:53 AM, Jonathan Worthington <[email protected]> wrote: > 438356039,46305 > 438356039,46339 > 438386087,56304 > <another 1.5 million or so entries here> > > I then create a recommender like: > > DataModel Model = new FileDataModel(Path);
So far, perfect. > ItemSimilarity SimilarityForItems = new PearsonCorrelationSimilarity(Model); Here's the problem, and it's not obvious. Pearson only works when you have ratings, and you don't. It'll end up being unable to compute any similarities, so no results. The fix is easy -- use LogLikelihoodSimilarity or TanimotoCoefficientSimilarity, which are defined even without ratings. I'd start with log-likelihood. If you're interested in more on this, I'll shamelessly plug early access to Mahout in Action. The parts available now include all of the coverage of recommenders, including much on this issue. http://www.manning.com/owen/ > ItemBasedRecommender Item = new GenericItemBasedRecommender(Model, > SimilarityForItems); > > And then do: > > List<RecommendedItem> Recommended = Item.mostSimilarItems(ItemID, HowMany); The rest is fine here. You can also compute recommendations of course, instead of most similar items.
