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From mishkinf <mishk...@hotmail.com>
Subject Mahout not giving recommendations with large data sets
Date Thu, 13 Aug 2009 19:03:23 GMT

I have been using mahout-0.1 release version and I am able to get
recommendations with datasets roughly 5 million and under but when I attempt
10 million or so no recommendations are given to me. Has anybody had this
problem? I'm not sure if I am just using the wrong recommender
settings/recommender or if I should just switch to trunk version or
something. Ideas? Suggestions?

I have tried item-item recommender, user-item recommenders.... nearest
neighborhood... tree clustering.. 
They all produce numerous recommendations with the smaller data sets. In
theory it should only get better with a larger data set. 

Currently I'm using item-item recommender with caching item similarities and
cashing recommender.. 

ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
CachingItemSimilarity cis = new CachingItemSimilarity(similarity,
dataModel);
recommender = new CachingRecommender(new
GenericItemBasedRecommender(dataModel, similarity));

......

I would like to have Mahout to work with 25-50 million rows of data but as
of yet 5 million is the best i can do. RAM has also been an issue with
larger data sets. 
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