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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-4712) Implementing ranking predictions for ALS
Date Mon, 21 Nov 2016 13:28:58 GMT

    [ https://issues.apache.org/jira/browse/FLINK-4712?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15683574#comment-15683574

ASF GitHub Bot commented on FLINK-4712:

Github user gaborhermann commented on the issue:

    Hi @thvasilo, of course, thanks for taking a look! :)

> 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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