Thank you Ted! Do plan to do any talks in Sweden soon?
Best, Niklas 2014-04-07 14:52 GMT+02:00 Ted Dunning <[email protected]>: > That book is a fine beginning, but doesn't have a lot of detail. > > Check out Pat's very nice demo site for more information. I have also > given a ton of talks on the subject. > > And, to answer your question, cooccurrence recommendation works great with > diverse sources of behavior. > > > > On Sun, Apr 6, 2014 at 8:40 PM, Niklas Ekvall <[email protected] > >wrote: > > > Thanks Pat! > > > > I did find a book by Ted Dunning and Ellen Friedman (Practical Machine > > Learning: Innovations in Recommendations) I guess I can us it to read > more > > about co-occurrence recommender or co-occurrence analysis. > > > > Best, Niklas > > > > > > > > 2014-04-06 19:37 GMT+02:00 Pat Ferrel <[email protected]>: > > > > > > > > > > On Apr 6, 2014, at 2:48 AM, Niklas Ekvall <[email protected]> > > > wrote: > > > > > > > > Hi Pat and Ted! > > > > > > > > Yes I agree with about the rank and MAP. But in this case, that is a > > good > > > > initial guess on the parameters *number of features* and *lambda*? > > > > > > 20 or 30 features depending on the variance in your data, more is > > > theoretically better but usually give rapidly diminishing returns. I > > forget > > > what lambdas we tried > > > > > > > > > > > Where can I find the best article about cooccurrence recommender? And > > can > > > > one use this approach for different types of data, e.g., ratings, > > > purchase > > > > histories or click histories? > > > > > > Absolutely, but remember that the data you train on is what you are > > > recommending. So if you train on detail-views (click paths) the > > recommender > > > will return items to look at, not necessarily the same as items to > > > purchase. If you train on what you want to recommend then all of the > > above > > > will work. > > > > > > If you want to train on click-paths and recommend purchase you probably > > > need a cross-recommender another discussion altogether. > > > > > > > > > > > Best, Niklas > > > > > > > > > > > > 2014-03-31 7:53 GMT+02:00 Ted Dunning <[email protected]>: > > > > > > > >> Yeah... what Pat said. > > > >> > > > >> Off-line evaluations are difficult. At most, they provide > directional > > > >> guidance to be refined using live A/B testing. Of course, A/B > testing > > > of > > > >> recommenders comes with a new set of tricky issues like different > > > >> recommenders learning from each other. > > > >> > > > >> On Sun, Mar 30, 2014 at 4:54 PM, Pat Ferrel <[email protected]> > > > wrote: > > > >> > > > >>> Seems like most people agree that ranking is more important than > > rating > > > >> in > > > >>> most recommender deployments. RMSE was used for a long time with > > > >>> cross-validation (partly because it was the choice of Netflix > during > > > the > > > >>> competition) but it is really a measure of total rating error. In > > the > > > >> past > > > >>> we've used mean-average-precision as a good measure of ranking > > quality. > > > >> We > > > >>> chose hold-out tests based on time, so something like 10% of the > most > > > >>> recent data was held out for cross-validaton and we measured > MAP@nfor > > > >>> tuning parameters. > > > >>> > > > >>> > > > >> > > > > > > http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision > > > >>> > > > >>> For our data (ecommerce shopping data) most of the ALS tuning > > > parameters > > > >>> had very little affect on MAP. However cooccurrence recommenders > > > >> performed > > > >>> much better using the same data. Unfortunately comparing two > > algorithms > > > >>> with offline tests is of questionable value. Still with nothing > else > > to > > > >> go > > > >>> on we went with the cooccurrence recommender. > > > >>> > > > >>> > > > >> > > > > > > > > > >
