Been building the scaffold for demonstrating the Solr + Mahout recommenders. Have mined rotten tomatoes for reviews and movies. Browsing, simple search, and item-item similarities are working in the UX.
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. 2) watch the users' detail views during a browsing session and make recs based on those in realtime. A sort of "are you looking for something like this?" recommender. For #1 I've seen several examples (BTW very few give instant recs). Not sure how they pick what to rate. It seems to me a mix of popular and the videos with the most varying ratings would be best. Since we have thumbs up and down it would be simple to find individual videos with a high degree of both love and hate. Intuitively this would seem to help find the birds of a feather among the reviewers and help put the user in with the right set with the fewest preferences required. #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. One nice thing about the implementation is that the Mahout Item-Based recommender output is available also so for any user in the training data we'll be able to show Solr recs and Mahout only recs side by side. Any thoughts on these experiments? Especially how to pick examples for the user in #1 to rate.
