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Posted to issues@flink.apache.org by "Domokos Miklós Kelen (JIRA)" <ji...@apache.org> on 2016/09/29 15:17:20 UTC
[jira] [Updated] (FLINK-4712) Implementing ranking predictions for
ALS
[ https://issues.apache.org/jira/browse/FLINK-4712?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Domokos Miklós Kelen updated FLINK-4712:
----------------------------------------
Description:
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 [issue todo] 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.
was:
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 [issue todo] 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.
> 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
>
> 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 [issue todo] 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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