Generally I would say 10s is a bit low, while a few 100s+ starts to make
sense. Of course it depends a lot on the specific use case, item catalogue
etc, user experience / platform, etc.
On Wed, Jun 26, 2019 at 3:57 PM Steve Pruitt <bpru...@opentext.com> wrote:
> I should have mentioned this is a synthetic dataset I create using some
> likelihood distributions of the rating values. I am only experimenting /
> learning. In practice though, the list of items is likely to be at least
> in the 10’s if not 100’s. Are even this item numbers to low?
>
>
>
> Thanks.
>
>
>
> -S
>
>
>
> *From:* Nick Pentreath <nick.pentre...@gmail.com>
> *Sent:* Wednesday, June 26, 2019 9:09 AM
> *To:* user@spark.apache.org
> *Subject:* Re: [EXTERNAL] - Re: Problem with the ML ALS algorithm
>
>
>
> If the number of items is indeed 4, then another issue is the rank of the
> factors defaults to 10. Setting the "rank" parameter < 4 will help.
>
>
>
> However, if you only have 4 items, then I would propose that using ALS (or
> any recommendation model in fact) is not really necessary. There is not
> really enough information as well as sparsity, to make collaborative
> filtering useful. And you could simply recommend all items a user has not
> rated and the result would be the same essentially.
>
>
>
>
>
> On Wed, Jun 26, 2019 at 3:03 PM Steve Pruitt <bpru...@opentext.com> wrote:
>
> Number of users is 1055
>
> Number of items is 4
>
> Ratings values are either 120, 20, 0
>
>
>
>
>
> *From:* Nick Pentreath <nick.pentre...@gmail.com>
> *Sent:* Wednesday, June 26, 2019 6:03 AM
> *To:* user@spark.apache.org
> *Subject:* [EXTERNAL] - Re: Problem with the ML ALS algorithm
>
>
>
> This means that the matrix that ALS is trying to factor is not positive
> definite. Try increasing regParam (try 0.1, 1.0 for example).
>
>
>
> What does the data look like? e.g. number of users, number of items,
> number of ratings, etc?
>
>
>
> On Wed, Jun 26, 2019 at 12:06 AM Steve Pruitt <bpru...@opentext.com>
> wrote:
>
> I get an inexplicable exception when trying to build an ALSModel with the
> implicit set to true. I can’t find any help online.
>
>
>
> Thanks in advance.
>
>
>
> My code is:
>
>
>
> ALS als = new ALS()
>
> .setMaxIter(5)
>
> .setRegParam(0.01)
>
> .setUserCol("customer")
>
> .setItemCol("item")
>
> .setImplicitPrefs(true)
>
> .setRatingCol("rating");
>
> ALSModel model = als.fit(training);
>
>
>
> The exception is:
>
> org.apache.spark.ml.optim.SingularMatrixException: LAPACK.dppsv returned 6
> because A is not positive definite. Is A derived from a singular matrix
> (e.g. collinear column values)?
>
> at
> org.apache.spark.mllib.linalg.CholeskyDecomposition$.checkReturnValue(CholeskyDecomposition.scala:65)
> ~[spark-mllib_2.11-2.3.1.jar:2.3.1]
>
> at
> org.apache.spark.mllib.linalg.CholeskyDecomposition$.solve(CholeskyDecomposition.scala:41)
> ~[spark-mllib_2.11-2.3.1.jar:2.3.1]
>
> at
> org.apache.spark.ml.recommendation.ALS$CholeskySolver.solve(ALS.scala:747)
> ~[spark-mllib_2.11-2.3.1.jar:2.3.1]
>
>
