Nice! On Dec 30, 2015 11:51 AM, "Pat Ferrel" <[email protected]> wrote:
> As many of you know Mahout-Samsara includes an interesting and important > extension to cooccurrence similarity, which supports cross-coossurrence and > log-likelihood downsampling. This, when combined with a search engine, > gives us a multimodal recommender. Some of us integrated Mahout with a DB > and search engine to create what we call (humbly) the Universal Recommender. > > We just completed a tool that measures the effects of what we call > secondary events or indicators using the Universal Recommender. It > calculates a ranking based precision metric called mean average > precision—MAP@k. We took a dataset from the Rotten Tomatoes web site of > “fresh”, and “rotten” reviews and combined that with data about the genres, > casts, directors, and writers of the various video items. This gave us the > indicators below: > like, video-id <== primary indicator > dislike, video-id > like-genre, genre-id > dislike-genre, genre-id > like-director, director-id > dislike-director, director-id > like-writer, writer-id > dislike-writer, writer-id > like-cast, cast-member-id > dislike-cast, cast-member-id > These aren’t necessarily what we would have chosen if we were designing > something from scratch but are possible to gather from public data. > > We have only ~5000 mostly professional reviewers with ~250k video items in > this dataset but have a larger one we are integrating. We are also writing > a white paper and blog post with some deeper analysis. There are several > tidbits of insight when you look deeper. > > The bottom line is that using most of the above indicators we were able to > get a 26% increase in MAP@1 over using only “like”. This is important > because the vast majority of recommenders can only really ingest one type > of indicator. > > http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html < > http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html> > > https://github.com/actionml/template-scala-parallel-universal-recommendation > < > https://github.com/actionml/template-scala-parallel-universal-recommendation > >
