The evaluation metrics are definitely useful. How do they differ from traditional IR metrics like prec@k and ndcg@k? -Xiangrui
On Mon, Aug 25, 2014 at 2:14 AM, Lizhengbing (bing, BIPA) < zhengbing...@huawei.com> wrote: > Hi: > > In paper “Item-Based Top-N Recommendation Algorithms”( > https://stuyresearch.googlecode.com/hg/blake/resources/10.1.1.102.4451.pdf), > there are two parameters measuring the quality of recommendation: HR and > ARHR. > > If I use ALS(Implicit) for top-N recommendation system, I want to check > it’s quality. ARHR and HR are two good quality measures. > > I want to contribute them to spark MLlib. So I want to know whether this > is meaningful? > > > > > > (1) If *n *is the total number of customers/users, the hit-rate of the > recommendation algorithm was computed as > > *hit-rate (HR) *= *Number of hits / n* > > > > (2)If *h *is the number of hits that occurred at positions *p*1, *p*2, *. > . . *, *p**h *within the *top-N *lists (i.e., 1 ≤ *p**i *≤ *N*), then the > average reciprocal hit-rank is equal to: > > *i* > > *.* >
