On Mon, Mar 25, 2013 at 9:52 AM, Sean Owen <[email protected]> wrote: > On Mon, Mar 25, 2013 at 1:41 PM, Koobas <[email protected]> wrote: > >> But the assumption works nicely for click-like data. Better still when > >> you can "weakly" prefer to reconstruct the 0 for missing observations > >> and much more strongly prefer to reconstruct the "1" for observed > >> data. > >> > > > > This does seem intuitive. > > How does the benefit manifest itself? > > In lowering the RMSE of reconstructing the interaction matrix? > > Are there any indicators that it results in better recommendations? > > Koobas > > In this approach you are no longer reconstructing the interaction > matrix, so there is no RMSE vs the interaction matrix. You're > reconstructing a matrix of 0 and 1. Because entries are weighted > differently, you're not even minimizing RMSE over that matrix -- the > point is to take some errors more seriously than others. You're > minimizing a *weighted* RMSE, yes. > > Yes of course the goal is better recommendations. This broader idea > is harder to measure. You can use mean average precision to measure > the tendency to predict back interactions that were held out. > > Is it better? depends on better than *what*. Applying algorithms that > treat input like ratings doesn't work as well on click-like data. The > main problem is that these will tend to pay too much attention to > large values. For example if an item was clicked 1000 times, and you > are trying to actually reconstruct that "1000", then a 10% error > "costs" (0.1*1000)^2 = 10000. But a 10% error in reconstructing an > item that was clicked once "costs" (0.1*1)^2 = 0.01. The former is > considered a million times more important error-wise than the latter, > even though the intuition is that it's just 1000 times more important. > > Better than algorithms that ignore the weight entirely -- yes probably > if only because you are using more information. But as in all things > "it depends". >
Let's say the following. Classic market basket. Implicit feedback. Ones and zeros in the input matrix, no weights in the regularization, lambda=1. What I will get is: A) a reasonable recommender, B) a joke of a recommender.
