Good god, you're quoting ME? One application of this is using several statistical recommenders, weighted. Another use case is that the GroupLens dataset comes with movie categories; combining a statistical recommender and a genre database sounds fun.
On Mon, Jun 6, 2011 at 1:53 PM, Steven Bourke <[email protected]> wrote: > Try something simple like this > > 1 - Identify which recommendation configurations you believe work best with > whatever data you currently have. > 2 - Run said configurations across your data, track the recommendation list > generated by each configuration. > 3 - Combine the scores for items across your various recommendation lists > 4 - Present results to whomever > > You should probably read the quickstart guide to get familiar with how to > set the recommendation aspect of mahout up (Check the wiki and examples in > trunk) > > On Mon, Jun 6, 2011 at 9:27 PM, jeff thomas <[email protected]> wrote: > >> Can anyone please provide details on how to stack algorithms? >> >> From: >> http://lucene.472066.n3.nabble.com/A-few-questions-regarding-content-based-recommenders-td2168516.html >> >> >> "Production recommendation systems use several algorythms and combine >> them with weights. This is called 'stacking'. You might wish to write >> a stacking version of Recommender. " >> > -- Lance Norskog [email protected]
