Will do. For what it's worth…
The project I'm working on is an online recommender for video content. You go to a site I'm creating, make some picks and get recommendations immediately online. The training data comes from mining rotten tomatoes for critics reviews. There are two actions, rotten & fresh. Was planning to toss the 'rotten' except for filtering them out of any recs but maybe they would work as A with an ensemble weight of -1? New thumbs up or down data would be put into the training set periodically--not online--using the process outlined below. On Jul 23, 2013, at 10:37 AM, Ted Dunning <[email protected]> wrote: This sounds great. Go for it. Put a comment on the design doc with a pointer to text that I should import. On Tue, Jul 23, 2013 at 9:39 AM, Pat Ferrel <[email protected]> wrote: I can supply: 1) a Maven based project in a public github repo as a baseline that creates the following 2) ingest and split actions, in-memory, single process, from text file, one line per preference 3) create DistributedRowMatrixes one per action (max of 3) with unified item and user space 4) create the 'similarity matrix' for [B'B] using LLR and [B'A] using matrix multiply/cooccurrence. 5) can take a stab at loading Solr. I know the Mahout side and the internal to external ID translation. The Solr side sounds pretty simple for this case. This pipeline lacks downsampling since I had to replace PreparePreferenceMatrixJob and potentially LLR for [B'A]. I assume Sebastian is the person to talk to about these bits? The job this creates uses the hadoop script to launch. Each job extends AbstractJob so runs locally or using HDFS or mapreduce (at least for the Mahout parts). I have some obligations coming up so if you want this I'll need to get moving. I can have the project ready on github in a day or two. May take longer to do the Solr integration and if someone has a passion for taking that bit on--great. This work is in my personal plans for the next couple weeks as it happens anyway. Let me know if you want me to proceed. On Jul 22, 2013, at 3:42 PM, Ted Dunning <[email protected]> wrote: On Mon, Jul 22, 2013 at 12:40 PM, Pat Ferrel <[email protected]> wrote: > Yes. And the combined recommender would query on both at the same time. > > Pat-- doesn't it need ensemble type weighting for each recommender > component? Probably a wishlist item for later? Yes. Weighting different fields differently is a very nice (and very easy feature).
