Yes that would be valid in the sense that the neighborhood based approaches are outputting a weighted average of prices here which is also a price. You would have to think about which similarity metrics are meaningful though.
The SVD has a perhaps undesirable behavior here. Because it treats the squared error of each datum equally it will tend to pay too much attention to large values. A 5% error produces 100 times more squared error when the value is large. Ted I think this is why you say the SVD is bad for count-like data? On Feb 6, 2013 8:28 AM, "万代豊" <[email protected]> wrote: > Hi > I also have a similar question regarding result interpretation based on how > we provide data to recommender. > Typcally , we provide rating data say in scale from 1-5 and get the result > in the same scale range.(and need to be consistent as Sean points out) > > If we assume the provided data with other dimension such as sales > revenue,price or some other physical dimension and can we still use the > recommender output as precdicted sales revenue or simulated possible price > in the same dimension scale? > I'm assuming user-based recommendation here. > > Looks like SVD recommender is more suitable for this purpose. > Apprecaite your advise. > Yutaka > > > > 2013/2/5 Sean Owen <[email protected]> > > > You don't have to fix a scale. But your data needs to be consistent. > > It wouldn't work to have users rate on a 1-5 scale one day, and 1-100 > > tomorrow (unless you go back and normalize the old data to 1-100). > > > > On Mon, Feb 4, 2013 at 3:56 PM, Zia mel <[email protected]> wrote: > > > Hi , is there a necessity to have a fix rating scale while running > > > recommendations or it can be dynamic based on the users' data ? > > > > > > Many Thanks > > >
