It looks like it works well. And it is gorgeous as well.
Nice work. Very nice. On Sun, Apr 6, 2014 at 8:59 PM, SriSatish Ambati <[email protected]>wrote: > It's quite good. Sri > > > On Sun, Apr 6, 2014 at 10:26 AM, Pat Ferrel <[email protected]> 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. > > > > > > >
