Ravi, we just merged https://issues.apache.org/jira/browse/SPARK-6642 and used the same lambda scaling as in 1.2. The change will be included in Spark 1.3.1, which will be released soon. Thanks for reporting this issue! -Xiangrui
On Tue, Mar 31, 2015 at 8:53 PM, Xiangrui Meng <men...@gmail.com> wrote: > I created a JIRA for this: > https://issues.apache.org/jira/browse/SPARK-6637. Since we don't have > a clear answer about how the scaling should be handled. Maybe the best > solution for now is to switch back to the 1.2 scaling. -Xiangrui > > On Tue, Mar 31, 2015 at 2:50 PM, Sean Owen <so...@cloudera.com> wrote: >> Ah yeah I take your point. The squared error term is over the whole >> user-item matrix, technically, in the implicit case. I suppose I am >> used to assuming that the 0 terms in this matrix are weighted so much >> less (because alpha is usually large-ish) that they're almost not >> there, but they are. So I had just used the explicit formulation. >> >> I suppose the result is kind of scale invariant, but not exactly. I >> had not prioritized this property since I had generally built models >> on the full data set and not a sample, and had assumed that lambda >> would need to be retuned over time as the input grew anyway. >> >> So, basically I don't know anything more than you do, sorry! >> >> On Tue, Mar 31, 2015 at 10:41 PM, Xiangrui Meng <men...@gmail.com> wrote: >>> Hey Sean, >>> >>> That is true for explicit model, but not for implicit. The ALS-WR >>> paper doesn't cover the implicit model. In implicit formulation, a >>> sub-problem (for v_j) is: >>> >>> min_{v_j} \sum_i c_ij (p_ij - u_i^T v_j)^2 + lambda * X * \|v_j\|_2^2 >>> >>> This is a sum for all i but not just the users who rate item j. In >>> this case, if we set X=m_j, the number of observed ratings for item j, >>> it is not really scale invariant. We have #users user vectors in the >>> least squares problem but only penalize lambda * #ratings. I was >>> suggesting using lambda * m directly for implicit model to match the >>> number of vectors in the least squares problem. Well, this is my >>> theory. I don't find any public work about it. >>> >>> Best, >>> Xiangrui >>> >>> On Tue, Mar 31, 2015 at 5:17 AM, Sean Owen <so...@cloudera.com> wrote: >>>> I had always understood the formulation to be the first option you >>>> describe. Lambda is scaled by the number of items the user has rated / >>>> interacted with. I think the goal is to avoid fitting the tastes of >>>> prolific users disproportionately just because they have many ratings >>>> to fit. This is what's described in the ALS-WR paper we link to on the >>>> Spark web site, in equation 5 >>>> (http://www.grappa.univ-lille3.fr/~mary/cours/stats/centrale/reco/paper/MatrixFactorizationALS.pdf) >>>> >>>> I think this also gets you the scale-invariance? For every additional >>>> rating from user i to product j, you add one new term to the >>>> squared-error sum, (r_ij - u_i . m_j)^2, but also, you'd increase the >>>> regularization term by lambda * (|u_i|^2 + |m_j|^2) They are at least >>>> both increasing about linearly as ratings increase. If the >>>> regularization term is multiplied by the total number of users and >>>> products in the model, then it's fixed. >>>> >>>> I might misunderstand you and/or be speaking about something slightly >>>> different when it comes to invariance. But FWIW I had always >>>> understood the regularization to be multiplied by the number of >>>> explicit ratings. >>>> >>>> On Mon, Mar 30, 2015 at 5:51 PM, Xiangrui Meng <men...@gmail.com> wrote: >>>>> Okay, I didn't realize that I changed the behavior of lambda in 1.3. >>>>> to make it "scale-invariant", but it is worth discussing whether this >>>>> is a good change. In 1.2, we multiply lambda by the number ratings in >>>>> each sub-problem. This makes it "scale-invariant" for explicit >>>>> feedback. However, in implicit feedback model, a user's sub-problem >>>>> contains all item factors. Then the question is whether we should >>>>> multiply lambda by the number of explicit ratings from this user or by >>>>> the total number of items. We used the former in 1.2 but changed to >>>>> the latter in 1.3. So you should try a smaller lambda to get a similar >>>>> result in 1.3. >>>>> >>>>> Sean and Shuo, which approach do you prefer? Do you know any existing >>>>> work discussing this? >>>>> >>>>> Best, >>>>> Xiangrui >>>>> >>>>> >>>>> On Fri, Mar 27, 2015 at 11:27 AM, Xiangrui Meng <men...@gmail.com> wrote: >>>>>> This sounds like a bug ... Did you try a different lambda? It would be >>>>>> great if you can share your dataset or re-produce this issue on the >>>>>> public dataset. Thanks! -Xiangrui >>>>>> >>>>>> On Thu, Mar 26, 2015 at 7:56 AM, Ravi Mody <rmody...@gmail.com> wrote: >>>>>>> After upgrading to 1.3.0, ALS.trainImplicit() has been returning vastly >>>>>>> smaller factors (and hence scores). For example, the first few product's >>>>>>> factor values in 1.2.0 are (0.04821, -0.00674, -0.0325). In 1.3.0, the >>>>>>> first few factor values are (2.535456E-8, 1.690301E-8, 6.99245E-8). This >>>>>>> difference of several orders of magnitude is consistent throughout both >>>>>>> user >>>>>>> and product. The recommendations from 1.2.0 are subjectively much better >>>>>>> than in 1.3.0. 1.3.0 trains significantly faster than 1.2.0, and uses >>>>>>> less >>>>>>> memory. >>>>>>> >>>>>>> My first thought is that there is too much regularization in the 1.3.0 >>>>>>> results, but I'm using the same lambda parameter value. This is a >>>>>>> snippet of >>>>>>> my scala code: >>>>>>> ..... >>>>>>> val rank = 75 >>>>>>> val numIterations = 15 >>>>>>> val alpha = 10 >>>>>>> val lambda = 0.01 >>>>>>> val model = ALS.trainImplicit(train_data, rank, numIterations, >>>>>>> lambda=lambda, alpha=alpha) >>>>>>> ..... >>>>>>> >>>>>>> The code and input data are identical across both versions. Did anything >>>>>>> change between the two versions I'm not aware of? I'd appreciate any >>>>>>> help! >>>>>>> --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
