Yes. Apologies for jargon and TLA<http://en.wikipedia.org/wiki/Three-letter_acronym> 's
On Thu, Apr 28, 2011 at 7:04 PM, Lance Norskog <[email protected]> wrote: > CTR == Clickthrough Rate > > On Thu, Apr 28, 2011 at 12:06 PM, Ted Dunning <[email protected]> > wrote: > > On Tue, Apr 26, 2011 at 8:00 PM, Stanley Xu <[email protected]> wrote: > > > >> ... I understood as the algorithm, the time in training only relies on > the > >> non-zero records, but per our test, there would be some overhead we > could > >> not ignore for thoso non-zero records, though the cost is sub-linear or > >> logit to the length of the hashed vector. > >> > > > > This is pretty close if we say "non-zero values". A record usually > refers > > to an entire training > > example. > > > > The extra work refers mostly to deferred regularization that eventually > has > > to be > > applied. My guess is that it is even less than log in the feature vector > > size. > > > > > >> And in CTR prediction, I am not pretty sure it will converge very > quickly. > >> > > > > I was saying this purely based on the number of features. > > > > > >> Because we will very possibly see some records has the almost same > feature > >> but different result in display ads. > > > > > > The algorithm can still converge to an estimate of the probability here. > > > > > >> But we will see the result in the > >> future. > > > > > > You have to be *very* careful about this to avoid prejudicing the model > > against > > recent impressions. If you have a fast feedback to the ad targeting > system, > > you > > can have severely instability. > > > > The key thing that you have to do to avoid these biases is to define a > > maximum > > delay before click for the purposes of modeling. You need to ignore all > > impressions > > younger than this delay (because they may still get a click) and you need > to > > ignore > > all clicks after this delay (to avoid bias in favor of old impressions). > > For on-line ads > > you can probably use a maximum delay of a few minutes because most clicks > > will > > happen by then. > > > > To find a good value for maximum delay, you should plot the CTR for a > bunch > > of > > ads versus delay. This will increase rapidly shortly after zero delay, > but > > then will > > level off. The ordering of ads by CTR is what you care about so you can > > follow the > > curves back and find the shortest delay where the ordering is clearly > > preserved. Use > > that as your maximum delay. Typically this is roughly where your CTR is > at > > about > > 80-90% of the final value. > > > > > > > > > >> (We were still working on creating a framework to digg all the > >> features we need from the log, I would like to share our experience by > >> using > >> Mahout SGD once we got our CTR prediction model release.) > >> > >> And for parallelize SGD, what do you mean for help with sparse inputs > that > >> exhibit long-tail frequency distribution? Would you like to share some > of > >> your ideas, Ted? > >> > >> Currently, what I could think about is split the data to multiple mapper > >> randomly and let every mapper to learn from the local data and get an > >> average on the whole model, or let multiple model to vote for every > >> feature's weight. A little like the idea of AdaBoost or RandomForest. > But I > >> am not a scientist or mathematician, so no idea if it is correct or not. > >> > >> > >> Thanks so much. > >> Stanley Xu > >> > >> > >> > >> On Tue, Apr 26, 2011 at 11:16 PM, Ted Dunning <[email protected]> > >> wrote: > >> > >> > On Mon, Apr 25, 2011 at 11:46 PM, Stanley Xu <[email protected]> > >> wrote: > >> > > >> > > 1 hour is acceptable, but I guess you misunderstand the data scale I > >> mean > >> > > here. The 900M records didn't mean 900M Bytes, but 900M lines of > >> training > >> > > set(900M training example.). If every training data has 1000 > dimension, > >> > it > >> > > means 900 million X 1000 X 16 B = 14TB. If we reduce the logs > collected > >> > to > >> > > 14 days, it would be still 2-3TB data. > >> > > > >> > > >> > Oops. Forgot that last multiplier. > >> > > >> > > >> > > Per our simple test, for 1000 dimension, 10M lines of record, it > will > >> > take > >> > > about 1-2 hours to do the training, so 90M lines of data will cost > at > >> > least > >> > > 90 hours, is that correct? > >> > > > >> > > >> > 10M x 1000 x 8 = 80 GB. > >> > > >> > 1-2 hours = (approx) 5000 seconds. So this is > >> > > >> > 80 GB / 5000 s = 80/5 MB /s = 16MB / s > >> > > >> > Yes. This is reasonable speed. I think you can get a small factor > >> faster > >> > than this with SGD. I have seen 100 million records with more > non-zero > >> > values than you describe with a training time of 3 hours. I would not > >> > expect even as much as a factor of 10 speedup here. > >> > > >> > > >> > > > >> > > And from the PPT you provided > >> > > http://www.slideshare.net/tdunning/sdforum-11042010 > >> > > You said it would take less than an hour for 20M data records for > >> > > numeric/category mixed dimensions. I am wondering, how many > dimensions > >> > per > >> > > record? > >> > > > >> > > >> > These are sparse records records with about a thousand non-zero > elements > >> > per > >> > record. > >> > > >> > > >> > But let's step back to your data for a moment. Where do these > thousand > >> > dimensions come from? Do you really have a thousand hand-built > features? > >> > Do you not have any sparse, text-like features? > >> > > >> > If you really only have a thousand dimensional problem, then I think > your > >> > model might exhibit early convergence. > >> > > >> > If not, it is quite possible to parallelize SGD, but this is only > likely > >> to > >> > help with sparse inputs that exhibit long-tail frequency distribution. > >> > > >> > > > > > > -- > Lance Norskog > [email protected] >
