> > On Sep 7, 2013, at 10:36 AM, Ted Dunning <ted.dunn...@gmail.com> wrote: > > On Fri, Sep 6, 2013 at 9:33 AM, Pat Ferrel <pat.fer...@gmail.com> wrote: > >> One of the unique things about the Solr recommender is online recs. Two >> scenarios come to mind: >> 1) ask the user to pick from among a list of videos, taking the picks as >> preferences and making recs. Make more and see if recs improve. > For #1, Ken's suggestion of clustering seems quite reasonable. The only > diff is that I would tend to pick something near the centroid of the > cluster *and* that is very popular. You need to have something people will > recognize. > > Clustering can be done by doing SVD or ALS on the user x thing matrix first > or by directly clustering the columns of the user x thing matrix after some > kind of IDF weighting. I think that only the streaming k-means currently > does well on sparse vectors. >
Was thinking about filtering out all but the top x% of items to get things the user is likely to have heard about if not seen. Do this before any factorizing or clustering. > >> #2 seems straightforward. No idea if it will be useful. If #2 doesn't seem >> useful is may be modified to become the typical, makes recs based on all >> reviews but also includes recent reviews not yet in the training data. >> That's OK since we'd want to do it anyway. >> > > For #2, I think that this is a great example of multi-modal > recommendations. You have browsing behavior and your tomatoes-reviews > behavior. Combining that allows you to recommend for people who have only > one kind of behavior. Of course, our viewing behavior will be very sparse > to start. Yes, that's why I'm not convinced it will be useful but an interesting experiment now that we have the online Solr recommender. Soon we'll have category and description metadata from the crawler. We can experiment with things like category boosting if a category trend emerges during the browsing session and I suspect it often does--maybe release date etc. The ease of mixing metadata with behavior is another thing worth experimenting with.
