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 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:
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:
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
On Fri, Jan 16, 2015 at 9:58 AM, Zork Sail zorks...@gmail.com 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
