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https://issues.apache.org/jira/browse/MAHOUT-1274?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13701380#comment-13701380]
Peng Cheng commented on MAHOUT-1274:
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BTW may I ask (noobishly) that why you have deprecated the
SlopeOneRecommender in the latest core-0.8 snapshot? i must have missed a
lot in previous mahout-development emails before i join so apologies if its
a stupid question.
SGD-based Online SVD recommender
--------------------------------
Key: MAHOUT-1274
URL: https://issues.apache.org/jira/browse/MAHOUT-1274
Project: Mahout
Issue Type: New Feature
Components: Collaborative Filtering
Reporter: Peng Cheng
Assignee: Sean Owen
Labels: collaborative-filtering, features,
machine_learning, svd
Original Estimate: 336h
Remaining Estimate: 336h
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.
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