Just wondering, what does mahout do for user/item pairs that do not have a rating? Does it fill it in with some average value? fill with zeros? something else?
On Apr 25, 2012, at 4:26 PM, Sean Owen wrote: > I don't know what the particular issue is; I imagine there's something > that needs some optimization in there. > > If you're definitely interested in ALS and recommenders, I don't feel > bad promoting our attempts to commercialize Mahout: Myrrix > (http://myrrix.com) is exactly an ALS-based recommender, and I know it > will crunch this data set into a model in 16 seconds on my laptop. > This part of it is also free / open source. > > Sean > > On Wed, Apr 25, 2012 at 9:28 PM, Daniel Quach <danqu...@cs.ucla.edu> wrote: >> I tried it again with 30 features and 3 iterations on the same data set, >> it's still running for 10+ minutes just to factorize for the SVDRecommender >> and has yet to complete. Perhaps it is my machine? >> >> I am running on a macbook air with 4GB of RAM and an intel i5 processor, I >> specified 2GB of memory for java. (-Xmx2048M) >> >> >> >> On Apr 25, 2012, at 12:25 PM, Sean Owen wrote: >> >>> There's not a hard limit; the hard limit you would run into is memory, >>> if anything. >>> >>> This sounds slow. It may be that this implementation could use some >>> optimization somewhere. Are you running many iterations or using a >>> large number of features? >>> >>> I have a different ALS implementation that finishes this data set (3 >>> iterations, 30 features -- quick and dirty) in more like 20 seconds. >>> Here's some info on a run on a much larger data set, using ALS, for >>> comparison: http://myrrix.com/example-performance/ >>> >>> On Wed, Apr 25, 2012 at 8:17 PM, Daniel Quach <danqu...@cs.ucla.edu> wrote: >>>> Regarding the factorization (I am using ALSWRFactorizer), is there a limit >>>> to how large a data set that can be factorized? >>>> >>>> I am trying to apply it on the 100K rating data set from group lens >>>> (approximately 1000 users by 1600 movies). >>>> >>>> It's been running for at least 10 minutes now, I am getting the feeling it >>>> might not be wise to apply the factorizer on a some of group lens's larger >>>> data sets... >>>> >>>> On Apr 18, 2012, at 1:09 PM, Sean Owen wrote: >>>> >>>>> This paper doesn't address how to compute the SVD. There are two >>>>> approaches implemented with SVDRecommender. One computes a SVD, one >>>>> doesn't :) Really it ought to be called something like >>>>> MatrixFactorizationRecommender. The SVD factorizer uses a fairly >>>>> simple expectation maximization approach. I don't know how well this >>>>> scales. The other factorizer uses alternating-least-squares. >>>>> >>>>> What you come out with are not 3 matrices, from an SVD, but 2. The "S" >>>>> matrix in the SVD of singular values is mashed into the left/right >>>>> singular vectors. >>>>> >>>>> So to answer your question now, the prediction expression is >>>>> essentially the same, with two caveats: >>>>> >>>>> 1. It shows it as the product of U, sqrt(S), sqrt(S), and V. What you >>>>> get out of the factorizer are really more like the "U" and "V" with >>>>> the two sqrt(S) bits already multiplied in. The product comes out the >>>>> same, there is a conceptual difference I suppose but not a practical >>>>> one. In both cases you're really just multiplying the matrix factors >>>>> all back together to make the predictions. >>>>> >>>>> 2. This model subtracts the customer average rating in the beginning, >>>>> and adds it back at the end here. The SVDRecommender doesn't do that, >>>>> because, quite crucially, it turns sparse data into dense data (all >>>>> the zeroes become non-zero) and this crushes scalability. >>>>> >>>>> The answer is "mostly the same thing" yes. In fact this is broadly how >>>>> all matrix factorization approaches work. >>>>> >>>>> On Wed, Apr 18, 2012 at 2:49 PM, Daniel Quach <danqu...@cs.ucla.edu> >>>>> wrote: >>>>>> I am basing my knowledge off this paper: >>>>>> http://www.grouplens.org/papers/pdf/webKDD00.pdf >>>>>> >>>>>> Your book provided algorithms for the user-based, item-based, and slope >>>>>> one recommendation, but none for the SVDRecommender (I'm guessing >>>>>> because it was experimental) >>>>>> >>>>>> Does the SVDRecommender just compute the resultant matrices and follow a >>>>>> formula similar to the one at the top of page 5 in the linked paper? I >>>>>> think I understand the process of SVD but I'm just wondering how it's >>>>>> exactly applied to obtain recommendations in mahout's case. >>>>>> >>>>>> >>>>>> On Apr 18, 2012, at 12:13 PM, Sean Owen wrote: >>>>>> >>>>>>> Yes you could call it a model-based approach. I suppose I was thinking >>>>>>> more of Bayesian implementations when I wrote that sentence. >>>>>>> >>>>>>> SVD is the Singular Value Decomposition -- are you asking what the SVD >>>>>>> is, or what matrix factorization is, or something about specific code >>>>>>> here? You can look up the SVD online. >>>>>>> >>>>>>> On Wed, Apr 18, 2012 at 12:49 PM, Daniel Quach <danqu...@cs.ucla.edu> >>>>>>> wrote: >>>>>>>> I had originally thought the experimental SVDrecommender in mahout was >>>>>>>> a model-based collaborative filtering technique. Looking at the book >>>>>>>> "Mahout in Action", it mentions that model-based recommenders are a >>>>>>>> future goal for mahout, which implies to me that the SVDRecommender is >>>>>>>> not considered model-based. >>>>>>>> >>>>>>>> How exactly does the SVDRecommender work in mahout? I can't seem to >>>>>>>> find any description of the algorithm underneath it >>>>>> >>>> >>
