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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-4712) Implementing ranking predictions for ALS
Date Thu, 24 Nov 2016 12:29:58 GMT

    [ https://issues.apache.org/jira/browse/FLINK-4712?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15693161#comment-15693161

ASF GitHub Bot commented on FLINK-4712:

Github user gaborhermann commented on a diff in the pull request:

    --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/pipeline/Predictor.scala
    @@ -72,14 +77,142 @@ trait Predictor[Self] extends Estimator[Self] with WithParameters
       def evaluate[Testing, PredictionValue](
           testing: DataSet[Testing],
    -      evaluateParameters: ParameterMap = ParameterMap.Empty)(implicit
    -      evaluator: EvaluateDataSetOperation[Self, Testing, PredictionValue])
    +      evaluateParameters: ParameterMap = ParameterMap.Empty)
    +      (implicit evaluator: EvaluateDataSetOperation[Self, Testing, PredictionValue])
         : DataSet[(PredictionValue, PredictionValue)] = {
         evaluator.evaluateDataSet(this, evaluateParameters, testing)
    +trait RankingPredictor[Self] extends Estimator[Self] with WithParameters {
    +  that: Self =>
    +  def predictRankings(
    +    k: Int,
    +    users: DataSet[Int],
    +    predictParameters: ParameterMap = ParameterMap.Empty)(implicit
    +    rankingPredictOperation : RankingPredictOperation[Self])
    +  : DataSet[(Int,Int,Int)] =
    +    rankingPredictOperation.predictRankings(this, k, users, predictParameters)
    +  def evaluateRankings(
    +    testing: DataSet[(Int,Int,Double)],
    +    evaluateParameters: ParameterMap = ParameterMap.Empty)(implicit
    +    rankingPredictOperation : RankingPredictOperation[Self])
    +  : DataSet[(Int,Int,Int)] = {
    +    // todo: do not burn 100 topK into code
    +    predictRankings(100, testing.map(_._1).distinct(), evaluateParameters)
    +  }
    +trait RankingPredictOperation[Instance] {
    +  def predictRankings(
    +    instance: Instance,
    +    k: Int,
    +    users: DataSet[Int],
    +    predictParameters: ParameterMap = ParameterMap.Empty)
    +  : DataSet[(Int, Int, Int)]
    +  * Trait for providing auxiliary data for ranking evaluations.
    +  *
    +  * They are useful e.g. for excluding items found in the training [[DataSet]]
    +  * from the recommended top K items.
    +  */
    +trait TrainingRatingsProvider {
    +  def getTrainingData: DataSet[(Int, Int, Double)]
    +  /**
    +    * Retrieving the training items.
    +    * Although this can be calculated from the training data, it requires a costly
    +    * [[DataSet.distinct]] operation, while in matrix factor models the set items could
    +    * given more efficiently from the item factors.
    +    */
    +  def getTrainingItems: DataSet[Int] = {
    +    getTrainingData.map(_._2).distinct()
    +  }
    +  * Ranking predictions for the most common case.
    +  * If we can predict ratings, we can compute top K lists by sorting the predicted ratings.
    +  */
    +class RankingFromRatingPredictOperation[Instance <: TrainingRatingsProvider]
    +(val ratingPredictor: PredictDataSetOperation[Instance, (Int, Int), (Int, Int, Double)])
    +  extends RankingPredictOperation[Instance] {
    +  private def getUserItemPairs(users: DataSet[Int], items: DataSet[Int], exclude: DataSet[(Int,
    +  : DataSet[(Int, Int)] = {
    +    users.cross(items)
    --- End diff --
    You're right. Although there's not much we can do generally to avoid this, we might be
able to optimize for matrix factorization. This solution works for *every* predictor that
predicts ratings, and we currently use it in ALS ([here](https://github.com/apache/flink/pull/2838/files/45c98a97ef82d1012062dbcf6ade85a8d566062d#diff-80639a21b8fd166b5f7df5280cd609a9R467)).
With a matrix factorization model *specifically*, we can avoid materializing all user-item
pairs as tuples, and compute the rankings more directly, and that might be more efficient.
So we could use a more specific `RankingPredictor` implementation in `ALS`. But even in that
case, we still need to go through all the items for a particular user to calculate the top
k items for that user.
    Also this is only calculated with for the users we'd like to give rankings to. E.g. in
a testing scenario, for the users in the test data which might be significantly less than
the users in the training data.
    I suggest to keep this anyway as this is general. We might come up with a solution that's
slightly efficient in most cases for MF models. Should put effort in working on it? What do
you think?

> Implementing ranking predictions for ALS
> ----------------------------------------
>                 Key: FLINK-4712
>                 URL: https://issues.apache.org/jira/browse/FLINK-4712
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Domokos Miklós Kelen
>            Assignee: Gábor Hermann
> We started working on implementing ranking predictions for recommender systems. Ranking
prediction means that beside predicting scores for user-item pairs, the recommender system
is able to recommend a top K list for the users.
> Details:
> In practice, this would mean finding the K items for a particular user with the highest
predicted rating. It should be possible also to specify whether to exclude the already seen
items from a particular user's toplist. (See for example the 'exclude_known' setting of [Graphlab
Create's ranking factorization recommender|https://turi.com/products/create/docs/generated/graphlab.recommender.ranking_factorization_recommender.RankingFactorizationRecommender.recommend.html#graphlab.recommender.ranking_factorization_recommender.RankingFactorizationRecommender.recommend]
> The output of the topK recommendation function could be in the form of {{DataSet[(Int,Int,Int)]}},
meaning (user, item, rank), similar to Graphlab Create's output. However, this is arguable:
follow up work includes implementing ranking recommendation evaluation metrics (such as precision@k,
recall@k, ndcg@k), similar to [Spark's implementations|https://spark.apache.org/docs/1.5.0/mllib-evaluation-metrics.html#ranking-systems].
It would be beneficial if we were able to design the API such that it could be included in
the proposed evaluation framework (see [5157|https://issues.apache.org/jira/browse/FLINK-2157]),
which makes it neccessary to consider the possible output type {{DataSet[(Int, Array[Int])]}}
or {{DataSet[(Int, Array[(Int,Double)])]}} meaning (user, array of items), possibly including
the predicted scores as well. See [4713|https://issues.apache.org/jira/browse/FLINK-4713]
for details.
> Another question arising is whether to provide this function as a member of the ALS class,
as a switch-kind of parameter to the ALS implementation (meaning the model is either a rating
or a ranking recommender model) or in some other way.

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