Rather than try impose ordinality on your data, you can think of "like", "dislike", "did not rate" as a categorical feature with a cardinality of three, which can be encoded using two binary features. All possibilities are fine, but the most logical is probably: rated=(0,1) and liked=(0,1).
So you just need to come up with the routine to encode these features. Hope this helps! Best, Angus -------------------------------------- Angus Macnab On Fri, Oct 9, 2015 at 3:54 PM, Shady Hanna <shadimamdouh...@gmail.com> wrote: > Hi , > > I have a data which is represented in like,user did not rate it, and > dislike, and I am not sure how I can represent this data to Mahout User > Based/Item Based Recommender System, and which user Similarity can be used > for such dataset. > > Would you please advise ? > > Thank you, > Best Regards, > Shady >
