One issue is that you broadcast the product vectors and then do a dot product one-by-one with the user vector.
You should try forming a matrix of the item vectors and doing the dot product as a matrix-vector multiply which will make things a lot faster. Another optimisation that is avalailable on 1.4 is a recommendProducts method that blockifies the factors to make use of level 3 BLAS (ie matrix-matrix multiply). I am not sure if this is available in The Python api yet. But you can do a version yourself by using mapPartitions over user factors, blocking the factors into sub-matrices and doing matrix multiply with item factor matrix to get scores on a block-by-block basis. Also as Ilya says more parallelism can help. I don't think it's so necessary to do LSH with 30,000 items. — Sent from Mailbox On Thu, Jun 18, 2015 at 6:01 AM, Ganelin, Ilya <ilya.gane...@capitalone.com> wrote: > Actually talk about this exact thing in a blog post here > http://blog.cloudera.com/blog/2015/05/working-with-apache-spark-or-how-i-learned-to-stop-worrying-and-love-the-shuffle/. > Keep in mind, you're actually doing a ton of math. Even with proper caching > and use of broadcast variables this will take a while defending on the size > of your cluster. To get real results you may want to look into locality > sensitive hashing to limit your search space and definitely look into > spinning up multiple threads to process your product features in parallel to > increase resource utilization on the cluster. > Thank you, > Ilya Ganelin > -----Original Message----- > From: afarahat [ayman.fara...@yahoo.com<mailto:ayman.fara...@yahoo.com>] > Sent: Wednesday, June 17, 2015 11:16 PM Eastern Standard Time > To: user@spark.apache.org > Subject: Matrix Multiplication and mllib.recommendation > Hello; > I am trying to get predictions after running the ALS model. > The model works fine. In the prediction/recommendation , I have about 30 > ,000 products and 90 Millions users. > When i try the predict all it fails. > I have been trying to formulate the problem as a Matrix multiplication where > I first get the product features, broadcast them and then do a dot product. > Its still very slow. Any reason why > here is a sample code > def doMultiply(x): > a = [] > #multiply by > mylen = len(pf.value) > for i in range(mylen) : > myprod = numpy.dot(x,pf.value[i][1]) > a.append(myprod) > return a > myModel = MatrixFactorizationModel.load(sc, "FlurryModelPath") > #I need to select which products to broadcast but lets try all > m1 = myModel.productFeatures().sample(False, 0.001) > pf = sc.broadcast(m1.collect()) > uf = myModel.userFeatures() > f1 = uf.map(lambda x : (x[0], doMultiply(x[1]))) > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/Matrix-Multiplication-and-mllib-recommendation-tp23384.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > ________________________________________________________ > The information contained in this e-mail is confidential and/or proprietary > to Capital One and/or its affiliates and may only be used solely in > performance of work or services for Capital One. The information transmitted > herewith is intended only for use by the individual or entity to which it is > addressed. If the reader of this message is not the intended recipient, you > are hereby notified that any review, retransmission, dissemination, > distribution, copying or other use of, or taking of any action in reliance > upon this information is strictly prohibited. If you have received this > communication in error, please contact the sender and delete the material > from your computer.
