an online SVD recommender is otherwise similar to an offline SVD recommender except that, upon receiving one or several new recommendations, it can add them into the training dataModel and update the result accordingly in real time.
an online SVD recommender should override setPreference(...) and removePreference(...) in AbstractRecommender such that the factorization result is updated in O(1) time and without retraining.
Right now the slopeOneRecommender is the only component possessing such capability.
Since SGD is intrinsically an online algorithm and its CF implementation is available in core-0.8 (See
MAHOUT-1089, MAHOUT-1272), I presume it would be a good time to convert it. Such feature could come in handy for some websites.
Implementation: Adding new users, items, or increasing rating matrix rank are just increasing size of user and item matrices. Reducing rating matrix rank involves just one svd. The real challenge here is that sgd is NO ONE-PASS algorithm, multiple passes are required to achieve an acceptable optimality and even more so if hyperparameters are bad. But here are two possible circumvents:
1. Use one-pass algorithms like averaged-SGD, not sure if it can ever work as applying stochastic convex-opt algorithm to non-convex problem is anarchy. But it may be a long shot.
2. Run incomplete passes in each online update using ratings randomly sampled (but not uniformly sampled) from latest dataModel. I don't know how exactly this should be done but new rating should be sampled more frequently. Uniform sampling will results in old ratings being used more than new ratings in total. If somebody has worked on this batch-to-online conversion before and share his insight that would be awesome. This seems to be the most viable option, if I get the non-uniform pseudorandom generator that maintains a cumulative uniform distribution I want.
I found a very old ticket (
MAHOUT-572) mentioning online SVD recommender but it didn't pay off. Hopefully its not a bad idea to submit a new ticket here.