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@n for > >>> 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. > >>> > >>> > >> > > >
