Hi Xiangrui, Could you please point to some reference for calculating prec@k and ndcg@k ?
prec is precision I suppose but ndcg I have no idea about... Thanks. Deb On Mon, Aug 25, 2014 at 12:28 PM, Xiangrui Meng <men...@gmail.com> wrote: > 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* > > > > *.* > > >
