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 <p...@occamsmachete.com> 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. > >
