Fascinating!!! Not too surprising really!!! On Nov 3, 2015 6:31 PM, "Suneel Marthi" <smar...@apache.org> wrote:
> Thanks Pat, very interesting indeed. > > On Tue, Nov 3, 2015 at 6:20 PM, Pat Ferrel <p...@occamsmachete.com> wrote: > > > A colleague of mine just build a MAP@k precision evaluator for the > Mahout > > based cooccurrence recommender we’ve been working on and we ran some data > > scraped from rottentomatoes.com <http://rottentomatoes.com/> They have > > “fresh” and “rotten” reviews tied to reviewer ids. > > > > A fair bit of discussion has gone on about how to use negative > > preferences. We have been saying that negative preferences might be > > predictive of positive preferences and the cross-cooccurrence code in the > > new SimilarityAnalysis.cooccurrence method can make the data usable. > > > > We took the RT data for two “actions”: “fresh" as the primary, the best > > indicator of preference, and “rotten” as the secondary indicator. We > found > > that MAP using only “fresh” was bettered by almost 20% when we included > > “rotten” as the secondary cross-cooccorrence action. For the strict out > > there we did not directly isolate the two actions, which is work > remaining > > so some of the lift might be due to just having more data but it’s a > really > > good first step because more data doesn't always translate to better > > performance and anyway it’s data you wouldn’t have otherwise. > > > > This opens up a new way to compare all sorts of other user signals, some > > long considered to be unusable by recommenders. Gender, location, > category > > preferences are now fair game for testing. > > > > BTW we used this recommender, which is based on Mahout Samsara’s matrix > > math, cooccurrence and LLR. > > https://github.com/pferrel/scala-parallel-universal-recommendation < > > https://github.com/pferrel/scala-parallel-universal-recommendation> >
