I have two separate Mahout recommenders set up, one providing recommendations based on user similarity and one based on item similarity. What I really want is a recommender that would base its recommendations on both dimensions, rather than just one.
Going through the documentation, I haven't been able to find any Recommender implementation which takes into account multiple dimensions. I could implement a basic version myself by taking the set intersection from the UserSimilarity recommendation set and the ItemSimilarity recommendation set, but it definitely wouldn't be the best way.
No it doesn't exist in the project. I think you could piece it together with some work. It would ultimately be based on a weighted average, where weights are a product of user-user and item-item similarity or something. You may find this just gets too slow to compute at run-time, or, if you want a more 'holistic' model you may find latent factor models more interesting anyway. But I have not tried it.