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], rank: Int, iterations: Int):
MatrixFactorizationModel
(
http://spark.apache.org/docs/1.0.0/api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$
)
This API requires `Rating` object:
Rating(user: Int, product: Int, rating: Double)
On the other hand documentation on `trainImplicit` tells: *Train a matrix
factorization model given an RDD of 'implicit preferences' ratings given by
users to some products, in the form of (userID, productID, **preference**)
pairs.*
When I set rating / preferences to `1` as in:
val ratings = sc.textFile(new File(dir, file).toString).map { line =>
val fields = line.split(",")
// format: (randomNumber, Rating(userId, productId, rating))
(rnd.nextInt(100), Rating(fields(0).toInt, fields(1).toInt, 1.0))
}
val training = ratings.filter(x => x._1 < 60)
.values
.repartition(numPartitions)
.cache()
val validation = ratings.filter(x => x._1 >= 60 && x._1 < 80)
.values
.repartition(numPartitions)
.cache()
val test = ratings.filter(x => x._1 >= 80).values.cache()
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:
val validationRmse = computeRmse(model, validation, numValidation)
/** Compute RMSE (Root Mean Squared Error). */
def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], n:
Long): Double = {
val predictions: RDD[Rating] = model.predict(data.map(x => (x.user,
x.product)))
val predictionsAndRatings = predictions.map(x => ((x.user, x.product),
x.rating))
.join(data.map(x => ((x.user, x.product), x.rating)))
.values
math.sqrt(predictionsAndRatings.map(x => (x._1 - x._2) * (x._1 -
x._2)).reduce(_ + _) / n)
}
So my question is: to what value should I set `rating` in:
Rating(user: Int, product: Int, rating: Double)
for implicit training (in `ALS.trainImplicit` method) ?
**Update**
With:
val alpha = 40
val lambda = 0.01
I get:
Got 1895593 ratings from 17471 users on 462685 products.
Training: 1136079, validation: 380495, test: 379019
RMSE (validation) = 0.7537217888106758 for the model trained with rank
= 8 and numIter = 10.
RMSE (validation) = 0.7489005441881798 for the model trained with rank
= 8 and numIter = 20.
RMSE (validation) = 0.7387672873747732 for the model trained with rank
= 12 and numIter = 10.
RMSE (validation) = 0.7310003522283959 for the model trained with rank
= 12 and numIter = 20.
The best model was trained with rank = 12, and numIter = 20, and its
RMSE on the test set is 0.7302343904091481.
baselineRmse: 0.0 testRmse: 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).
