Yes, relevance is always 1. The label is not a relevance score so
don't think it's valid to use it as such.

On Mon, Sep 19, 2016 at 4:42 AM, Jong Wook Kim <> wrote:
> Hi,
> I'm trying to evaluate a recommendation model, and found that Spark and
> Rival give different results, and it seems that Rival's one is what Kaggle
> defines:
> Am I using RankingMetrics in a wrong way, or is Spark's implementation
> incorrect?
> To my knowledge, NDCG should be dependent on the relevance (or preference)
> values, but Spark's implementation seems not; it uses 1.0 where it should be
> 2^(relevance) - 1, probably assuming that relevance is all 1.0? I also tried
> tweaking, but its method to obtain the ideal DCG also seems wrong.
> Any feedback from MLlib developers would be appreciated. I made a
> modified/extended version of RankingMetrics that produces the identical
> numbers to Kaggle and Rival's results, and I'm wondering if it is something
> appropriate to be added back to MLlib.
> Jong Wook

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