Hi Michael, I can help check the current implementation. Would you please go to https://spark-project.atlassian.net/browse/SPARK and create a ticket about this issue with component "MLlib"? Thanks!
Best, Xiangrui On Tue, Mar 11, 2014 at 3:18 PM, Michael Allman <m...@allman.ms> wrote: > Hi, > > I'm implementing a recommender based on the algorithm described in > http://www2.research.att.com/~yifanhu/PUB/cf.pdf. This algorithm forms the > basis for Spark's ALS implementation for data sets with implicit features. > The data set I'm working with is proprietary and I cannot share it, however > I can say that it's based on the same kind of data in the paper---relative > viewing time of videos. (Specifically, the "rating" for each video is > defined as total viewing time across all visitors divided by video > duration). > > I'm seeing counterintuitive, sometimes nonsensical recommendations. For > comparison, I've run the training data through Oryx's in-VM implementation > of implicit ALS with the same parameters. Oryx uses the same algorithm. > (Source in this file: > https://github.com/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/AlternatingLeastSquares.java) > > The recommendations made by each system compared to one other are very > different---moreso than I think could be explained by differences in initial > state. The recommendations made by the Oryx models look much better, > especially as I increase the number of latent factors and the iterations. > The Spark models' recommendations don't improve with increases in either > latent factors or iterations. Sometimes, they get worse. > > Because of the (understandably) highly-optimized and terse style of Spark's > ALS implementation, I've had a very hard time following it well enough to > debug the issue definitively. However, I have found a section of code that > looks incorrect. As described in the paper, part of the implicit ALS > algorithm involves computing a matrix product YtCuY (equation 4 in the > paper). To optimize this computation, this expression is rewritten as YtY + > Yt(Cu - I)Y. I believe that's what should be happening here: > > https://github.com/apache/incubator-spark/blob/v0.9.0-incubating/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala#L376 > > However, it looks like this code is in fact computing YtY + YtY(Cu - I), > which is the same as YtYCu. If so, that's a bug. Can someone familiar with > this code evaluate my claim? > > Cheers, > > Michael
