The assumption of implicit feedback model is that the unobserved
ratings are more likely to be negative. So you may want to add some
negatives for evaluation. Otherwise, the input ratings are all 1 and
the test ratings are all 1 as well. The baseline predictor, which uses
the average rating (that i
On Fri, Jan 16, 2015 at 9:58 AM, Zork Sail wrote:
> And then train ALSL:
>
> val model = ALS.trainImplicit(ratings, rank, numIter)
>
> I get RMSE 0.9, which is a big error in case of preferences taking 0 or 1
> value:
This is likely the problem. RMSE is not an appropriate evaluation
metric w
e: 0.7302343904091481
The best model improves the baseline by -Infinity%.
Which is still a big error, I guess. Also I get strange baseline
improvement where baseline model is simply mean (1).
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I am trying to use Spark MLib ALS with implicit feedback for collaborative
filtering. Input data has only two fields `userId` and `productId`. I have
**no product ratings**, just info on what products users have bought,
that's all. So to train ALS I use:
def trainImplicit(ratings: RDD[Rating],
