The reboot of that old mr engines in Mahout-Samsara is what we call Correlated Cross-Occurrence (CCO) this is the core of a mutli-modal recommender engine that can use almost any information about the user, context, or items to make recommendations. It is the first Open Source version of this algorithm as far as I know.
This http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html describes the core engine in Mahout. An end-to-end implementation with event ingest, training, and scalable deployment under Apache 2 license is here: https://github.com/actionml/template-scala-parallel-universal-recommendation/tree/v0.3.0 Notice many improvements over the old (soon to be deprecated) mr implementation: realtime queries—fast responses uses realtime usage data to capture most recent user behavior—allows person recs to anonymous new users uses as much of the user’s clickstream and context as makes sense—the multi-modal cross-occurrence part can use preferences for categories, tags, genres. brands, location. devices, etc—more multi-modal cross-occurrence We have recently begun testing the multi-modality features against public data and will be publishing some very encouraging results. One interesting finding is where we took user “likes” and “dislikes” from reviews on rottentomatoes and found that “dislikes” along with genre preferences gave us a 23% increase in mean average precision over using “likes” alone. Yes, that means dislikes may predict likes. Unless a recommender is multi-modal it can really only use one user action—“likes” in this example. So ALS is not multi-modal. > On Feb 19, 2016, at 1:30 AM, Lee S <sle...@gmail.com> wrote: > > @Adi this link is for als algorithm, not the item-based implementation. > > > On Fri, Feb 19, 2016 at 1:09 PM, Adi Haviv <adiha...@gmail.com> wrote: > >> collaborative filtering - >> https://codeascraft.com/2014/11/17/personalized-recommendations-at-etsy/ >> >> On Fri, Feb 19, 2016 at 8:46 AM, Lee S <sle...@gmail.com> wrote: >> >>> Hi: >>> Does anybody know which paper the mr algorithm is based on? >>> >> >> >> >> -- >> Adi Haviv. >>
