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From Zork <zorks...@gmail.com>
Subject MLib: How to set preferences for ALS implicit feedback in Collaborative Filtering?
Date Fri, 16 Jan 2015 11:10:34 GMT
    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).




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