Ok...just to make sure I have RowMatrix[SparseVector] where rows are ~ 60M and columns are 10M say with billion data points...
I have another version that's around 60M and ~ 10K... I guess for the second one both all pair and dimsum will run fine... But for tall and wide, what do you suggest ? can dimsum handle it ? I might need jaccard as well...can I plug that in the PR ? On Fri, Sep 5, 2014 at 7:48 PM, Reza Zadeh <r...@databricks.com> wrote: > You might want to wait until Wednesday since the interface will be > changing in that PR before Wednesday, probably over the weekend, so that > you don't have to redo your code. Your call if you need it before a week. > Reza > > > On Fri, Sep 5, 2014 at 7:43 PM, Debasish Das <debasish.da...@gmail.com> > wrote: > >> Ohh cool....all-pairs brute force is also part of this PR ? Let me pull >> it in and test on our dataset... >> >> Thanks. >> Deb >> >> >> On Fri, Sep 5, 2014 at 7:40 PM, Reza Zadeh <r...@databricks.com> wrote: >> >>> Hi Deb, >>> >>> We are adding all-pairs and thresholded all-pairs via dimsum in this PR: >>> https://github.com/apache/spark/pull/1778 >>> >>> Your question wasn't entirely clear - does this answer it? >>> >>> Best, >>> Reza >>> >>> >>> On Fri, Sep 5, 2014 at 6:14 PM, Debasish Das <debasish.da...@gmail.com> >>> wrote: >>> >>>> Hi Reza, >>>> >>>> Have you compared with the brute force algorithm for similarity >>>> computation with something like the following in Spark ? >>>> >>>> https://github.com/echen/scaldingale >>>> >>>> I am adding cosine similarity computation but I do want to compute an >>>> all pair similarities... >>>> >>>> Note that the data is sparse for me (the data that goes to matrix >>>> factorization) so I don't think joining and group-by on (product,product) >>>> will be a big issue for me... >>>> >>>> Does it make sense to add all pair similarities as well with dimsum >>>> based similarity ? >>>> >>>> Thanks. >>>> Deb >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Fri, Apr 11, 2014 at 9:21 PM, Reza Zadeh <r...@databricks.com> >>>> wrote: >>>> >>>>> Hi Xiaoli, >>>>> >>>>> There is a PR currently in progress to allow this, via the sampling >>>>> scheme described in this paper: stanford.edu/~rezab/papers/dimsum.pdf >>>>> >>>>> The PR is at https://github.com/apache/spark/pull/336 though it will >>>>> need refactoring given the recent changes to matrix interface in MLlib. >>>>> You >>>>> may implement the sampling scheme for your own app since it's much code. >>>>> >>>>> Best, >>>>> Reza >>>>> >>>>> >>>>> On Fri, Apr 11, 2014 at 9:17 PM, Xiaoli Li <lixiaolima...@gmail.com> >>>>> wrote: >>>>> >>>>>> Hi Andrew, >>>>>> >>>>>> Thanks for your suggestion. I have tried the method. I used 8 nodes >>>>>> and every node has 8G memory. The program just stopped at a stage for >>>>>> about >>>>>> several hours without any further information. Maybe I need to find >>>>>> out a more efficient way. >>>>>> >>>>>> >>>>>> On Fri, Apr 11, 2014 at 5:24 PM, Andrew Ash <and...@andrewash.com> >>>>>> wrote: >>>>>> >>>>>>> The naive way would be to put all the users and their attributes >>>>>>> into an RDD, then cartesian product that with itself. Run the >>>>>>> similarity >>>>>>> score on every pair (1M * 1M => 1T scores), map to (user, (score, >>>>>>> otherUser)) and take the .top(k) for each user. >>>>>>> >>>>>>> I doubt that you'll be able to take this approach with the 1T pairs >>>>>>> though, so it might be worth looking at the literature for recommender >>>>>>> systems to see what else is out there. >>>>>>> >>>>>>> >>>>>>> On Fri, Apr 11, 2014 at 9:54 PM, Xiaoli Li <lixiaolima...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi all, >>>>>>>> >>>>>>>> I am implementing an algorithm using Spark. I have one million >>>>>>>> users. I need to compute the similarity between each pair of users >>>>>>>> using >>>>>>>> some user's attributes. For each user, I need to get top k most >>>>>>>> similar >>>>>>>> users. What is the best way to implement this? >>>>>>>> >>>>>>>> >>>>>>>> Thanks. >>>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
