Some of the references for the newer cooccurrence recommender that we now
suggest you use are at the top of the page here:
http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
There are many benefits of this new method including at its core a new
similarity algorithm that relies on log-likelihood (LLR) calculated
cooccurrence strength. These don’t suffer from the problems you mention.
On Mar 8, 2015, at 8:06 AM, Ted Dunning wrote:
On Sat, Mar 7, 2015 at 3:05 AM, Tevfik Aytekin
wrote:
> There can be two solutions:
> 1. There should be a parameter n, which determines the minimum number
> of common ratings needed to compute a similarity otherwise the system
> should return NaN.
> 2. The similarity should be computed using all the ratings, for the
> above two vectors, the cosine similarity should be
>
> (3*5+2*4)/(sqrt(3^2+4^2+2^2)+sqrt(3^2+5^2+2^2+4^2))
>
or
3. Use the more modern and scalable recommendation methods.
