There is already code in Mahout that splits a dataset into training- and testset: org.apache.mahout.cf.taste.hadoop.als.eval.DatasetSplitter and there is already an evaluator for factorization based recommendations: org.apache.mahout.cf.taste.hadoop.als.eval.ParallelFactorizationEvaluator
This might help as a starting point for implementing evaluation of RecommenderJob. --sebastian On 17.10.2011 09:39, WangRamon wrote: > > Hi Sean Do you mean that I should take the concept from the standalone one, > keep some real data, let's say 20% of all data, do recommend computation on > the other 80%, and finally do a comparation. CheersRamon > > Date: Mon, 17 Oct 2011 08:02:37 +0100 >> Subject: Re: Does Mahout provide a way to evaluate a distributed Recommender >> running on Hadoop? >> From: [email protected] >> To: [email protected] >> >> There is not one, though you could probably adapt the evaluation code >> without a great deal of trouble. The concept is the same; the >> implementation is quite different. You would withhold some data, and >> then compute the value of that withheld data and compare with the >> original. >> >> 2011/10/17 WangRamon <[email protected]>: >>> >>> >>> >>> >>> Hi Guys >>> >>> We're going to evaluate how good a distributed (on Hadoop) recommender is, >>> i found Mahout provides some stand alone implementation to evaluate a >>> recommender, so is there a distributed implementation we can use in a >>> Hadoop environment, thanks a lot. >>> >>> BTW, if there is not such an implementation, do we have any solution/idea >>> on how to implement one? >>> >>> Cheers >>> Ramon >
