This can actually be simplified a bit by using ItemSimilarityJob to call RowSimilarityJob.
Nice work overall. On Sun, Apr 6, 2014 at 10:21 PM, Andrew Musselman < [email protected]> wrote: > Pat, do you still want help putting this into a new mahout/examples, or > work out how to do the distribution via "github pointer"? There's an open > bug for that. > > > On Apr 6, 2014, at 1:13 PM, Sebastian Schelter <[email protected]> wrote: > > > > The top 3 recommendations "based on videos you liked" are very good! > > > > Nice job. > > > > > >> On 04/06/2014 07:26 PM, Pat Ferrel wrote: > >> After having integrated several versions of the Mahout and Myrrix > recommenders at fairly large scale. I was interested in solving three > problems that these did not directly provide for: > >> 1) realtime queries for recs using data not yet incorporated into the > training set. Myrrix allows this but Mahout using the hadoop mr version > does not. > >> 2) cross-recommendations from two or more action types (say purchase > and detail-view) > >> 3) blending metadata and user preference data to return recs (for > example category & user preferences => recs) > >> > >> Using Solr + Mahout provided an amazingly flexible and performant way > to do this. Ted wrote about his experience with this basic approach in his > recent book. Take user preferences, run them through RowSimilarityJob and > you get an item by item similarity Matrix. This is the core of an > item-based cooccurrence recommender. If you take the similarity matrix, and > convert it into a list of tokens per row, you have something Solr can > index. If you then use a user’s history as a query on the indexed data you > get an ordered list of recommendations. > >> > >> When I set out to do #1 and #3 the need for CF data AND metadata was > the first problem. So I mined the web for video reviews and video metadata. > Then logging any users who visit the site will lead to data for #2 and #1. > >> > >> The demo site is https://guide.finderbots.com and instructions are at > the end of this for anyone who would like to test it out. As a crude user > test there is a procedure we ask you to follow to help gather quality of > recommendations data. It’s running out of my closet over Comcast so if it’s > down I may have tripped over a cord, sorry try again later. > >> > >> There are a bunch of different methods for making recs illustrated on > the site. One method that illustrates blending metadata uses preference > data from you, and metadata to bias and filter recs. Imagine that you have > trained the system with your preferences by making some video picks. Now > imagine you’d like to get recommendations for Comedies from Neflix based on > your previous video preferences. This is done with a single Solr query on > indexed video fields that hold genre, similar videos (from the similarity > matrix), and sources. The query finds similar videos to the ones you have > liked, with the genre “Comedy” boosted by some amount, but only those that > have at least one source = “Netflix”. > >> > >> I’ll be doing some blog posts covering the specifics of how each rec > type is done, the site and DB architecture, and Solr setup. > >> > >> The project uses the Solr recommender prep code here: > https://github.com/pferrel/solr-recommender > >> > >> BTW I plan to publish obfuscated usage data in the github repo. > >> > >> begin form letter ======================================= > >> > >> Please use a very newly updated browser (latest Firefox, Chrome, > Safari, and nothing older than IE10) the site doesn’t yet check browser > compatibility but relies on HTML5 and CSS3 rather heavily. > >> > >> 1) go to https://guide.finderbots.com/users/sign_up to create an > account > >> 2) go to https://guide.finderbots.com/trainers to ’train' the > recommender hit thumbs up on videos you like. There are 20 pages of > training videos, you can leave at any time but if you can go through them > all it would be appreciated. > >> 3) go to https://guide.finderbots.com/guides/recommend to immediately > get personalized recs from your training data. If you completed the trainer > check the top line of recs, count how many are videos you liked or would > like to see. Scroll right or left to see a total of 24 in four batches of > 6. If you could report to me the total you thought were good recs it would > be greatly appreciated. > >> 4) browse videos by various criteria here: > https://guide.finderbots.com/guides These are not recommendations, they > are simply a catalog. > >> 5) control how you browse videos by clicking the gears icon. You can > set all videos to be from one or more sources here. If you choose Netflix > alone (don’t forget to uncheck ‘all’) then recs and browsed videos will all > be available on Netflix. > > >
